Brand Stories
Travel Advisor Success Story: Ariel Chavez, Cruise Planners
Ariel Chavez (Source: Ariel Chavez)
Travel Advisor Success Stories focus on veteran advisors and how they achieved success. Here’s a look at Cruise Planners Franchise Owner Ariel Chavez.
How did you get your start as a travel advisor?
I’m originally from La Paz, Bolivia. Traveling and living abroad were dreams of mine since I was a little boy. My parents gave me the great opportunity to study abroad, and that’s how I arrived in Mobile, Ala.
After working on highway design as a civil engineer for almost 10 years, I wanted to take a sabbatical year and travel around the world. I had the idea to open my own engineering business to keep my professional license. While I was researching how to open and run a business, I came across Cruise Planners. It sounded very intriguing, so I did some research on becoming a travel advisor, and I decided to buy a Cruise Planners franchise and the rest is history – no regrets.
How did you build your business over the years?
The first two years were probably the most demanding in terms of networking and letting people know I had opened my own business in a completely different industry. Some people thought I was nuts to leave my engineering job and open a travel agency.
During those first years I was out and about everywhere, telling people about my new business. New clients and bookings started to come in, and then those clients came back to book other trips and brought family and friends.
Word of mouth is definitely a powerful tool to grow your business. Also, Cruise Planners offers a list of ideas, suggestions and training you can take advantage of – which were also helpful tools.
What characteristics make you a successful advisor?
I establish a good relationship with my clients so I can learn about their travel style, and I listen carefully to their feedback. When I get a new client, I send them a questionnaire, which I have developed over the years. Their answers help me better understand what their needs and wants are.
I try to be the best matchmaker I can be. If they love their experience, they will likely come back for more. They might not know of other or newer brands entering the market, and it’s my job as their verified travel advisor to inform and guide them as best I can.
Many of our clients are now more like friends. They send us Christmas cards and even gifts for our kids. I consider myself fortunate to work with such kind and wonderful people.
What have your greatest challenges been?
My husband and I are foster care parents, so sometimes fulfilling our parenting obligations while trying to run a small business can be challenging. I think effective time management is not easy to accomplish. With the help of my Cruise Planners’ coach and CP Maxx, Cruise Planners’ CRM, I have been able to identify and prioritize those tasks.
What have your greatest accomplishments been?
Last year my team and I had a holiday party to celebrate our best year in business. I felt such joy realizing we have built a successful small business from zero. I also remember the first time we went over $1 million in annual business. When I started, I never thought that would be possible – but it was!
What tips can you provide advisors new to the industry?
Be consistent and curious, and set achievable goals each year. Find a good mentor or coach who is willing to help you. Cruise Planners provides experienced coaches and a full support team. Talk to other senior agents and pick their brain. Adjust their ideas to your own needs and business goals. Keep educating yourself and travel so you have knowledge and first-hand experience to share with your clients.
For the latest travel news, updates and deals, subscribe to the daily TravelPulse newsletter.
Topics From This Article to Explore
Key Points
-
The chip industry is booming thanks to AI.
-
Advanced Micro Devices is seeing margins and earnings soar as its data center business expands.
-
Broadcom is meeting insatiable demand for custom AI chips and networking solutions for advanced AI workloads.
Artificial intelligence (AI) is impacting every sector of the economy, so there are several ways investors can profit from this opportunity. But recent earnings results show that top semiconductor companies are still well positioned to deliver outstanding returns for long-term investors.
The AI chip market is expected to grow at an annualized rate of 24% through 2029 to reach $311 billion, according to MarketsandMarkets. If you have $3,000 you’re looking to invest right now, here are two chip stocks to consider buying and holding for the long term.
Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now. Learn More »
Image source: Getty Images.
1. Advanced Micro Devices
Advanced Micro Devices(NASDAQ: AMD) has become a widely used brand of chips in the consumer PC market. Its Ryzen processors have taken significant market share from Intel. But it’s also one of only two suppliers, along with Nvidia, of general-purpose graphics processing units (GPUs) that are used for AI workloads.
While Nvidia has a commanding lead in GPUs, it’s not going to control 100% of the market. This leaves a substantial opportunity for the runner-up in this market to do well. AMD’s data center business is booming, with segment revenue up 57% year over year in the first quarter.
AMD is meeting demand for cost-effective alternatives in the chip market. Oracle is experiencing tremendous growth in its cloud infrastructure business right now, and it’s a key partner for AMD. Oracle’s cloud infrastructure will offer up to 131,072 AMD Instinct MI355X GPUs for AI. AMD has already announced the MI400 series for launch next year, which will enable even better performance for AI training and inferencing.
As data center sales make up a larger mix of AMD’s total revenue, it is pushing margins up. Higher margins drove a 55% year-over-year increase in adjusted earnings last quarter. Given the long-term opportunity in the AI chip market, which AMD estimates at $500 billion, investors are undervaluing AMD’s future earnings.
The stock is trading at a forward price-to-earnings (P/E) multiple of 38 on 2025 earnings estimates. But this multiple drops to 25 on 2026 estimates. As AMD continues to expand margins from growth in its data center business, the stock could offer significant upside over the next few years and beyond.
2. Broadcom
Beyond the surging demand for general-purpose chips that AMD supplies, there is growing demand for chips designed for specialized tasks. Broadcom(NASDAQ: AVGO) is one of the best stocks to profit from the demand for custom chip solutions.
Broadcom has been a top-performing semiconductor company for years, supplying components for many markets, including Apple‘s iPhone. But demand for its application-specific integrated circuits (ASICs) for AI is off the charts.
The company’s AI chip revenue grew 46% year over year in the most recent quarter. As demand for custom ASICs grows, it also fuels demand for networking products that can handle faster data transfer, which is needed for next-level AI performance.
Broadcom’s new Tomahawk 6 Ethernet switch has enough data capacity to support 100,000 AI chips working together to train the next-generation AI models. The company’s networking business posted revenue growth of 170% year over year last quarter, representing 40% of its AI-related revenue.
However, management sees the demand for its custom AI chips outpacing sales of its networking products over time. It’s a huge opportunity, as evidenced by Broadcom’s momentum. Management expects its AI growth to remain steady through fiscal 2026, which could support new highs for the stock.
Broadcom earns very high margins, so the favorable demand outlook points to robust earnings over the next year. The stock trades at 41 times this year’s consensus earnings estimate, but that multiple drops to 33 on next year’s estimate. These are not cheap valuation multiples, but the investment in AI technology is pointing to substantial growth in the coming years for leading chipmakers, and that should support excellent returns for investors.
Should you invest $1,000 in Advanced Micro Devices right now?
Before you buy stock in Advanced Micro Devices, consider this:
The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Advanced Micro Devices wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years.
Consider when Netflix made this list on December 17, 2004… if you invested $1,000 at the time of our recommendation, you’d have $687,149!* Or when Nvidia made this list on April 15, 2005… if you invested $1,000 at the time of our recommendation, you’d have $1,060,406!*
Now, it’s worth noting Stock Advisor’s total average return is 1,069% — a market-crushing outperformance compared to 180% for the S&P 500. Don’t miss out on the latest top 10 list, available when you join Stock Advisor.
*Stock Advisor returns as of July 15, 2025
Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. John Ballard has positions in Advanced Micro Devices and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Apple, Intel, Microsoft, Nvidia, and Oracle. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft, short August 2025 $24 calls on Intel, and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.
The market has returned to its highs, along with many top artificial intelligence (AI) names. However, another market dip could always be around the corner.
Let’s look at three top AI stocks that have made strong runs that would be good buys on a pullback.
Image source: Getty Images.
1. Palantir
With a high valuation but attractive growth opportunities, Palantir Technologies (PLTR -0.34%) is a stock that would be attractive on a dip. The company has emerged as one of the market’s most compelling AI growth stories, and its momentum has been gaining speed.
In the first quarter, Palantir posted its seventh consecutive period of accelerating growth, with revenue up 39%. The rise is being led by its U.S. commercial segments, which saw sales jump 71% and the value of future deals soar 127%.
While there is a lot of talk about which company is building the best AI model, Palantir is focused on something far more practical: making AI useful. Its Artificial Intelligence Platform (AIP) uses AI models to help solve real-world problems.
AIP does this by gathering data and then connecting it to physical assets and operational workflows, allowing companies to make AI more useful. As a result, the platform is being used for a growing list of purposes, including hospitals monitoring for sepsis, insurers using it in their underwriting, and energy companies optimizing their pipeline infrastructure.
The company’s largest customer is the U.S. government, which is starting to embrace AI to become more efficient. Last quarter, Palantir’s government revenue climbed 45%.
The company also recently landed a major deal with NATO, expanding into international defense just as Europe ramps up military spending. That gives it three potential growth engines: domestic commercial enterprises, the U.S. government, and now the international public sector.
Yes, the stock is expensive by traditional metrics, but Palantir looks like it’s laying the groundwork to become one of the next megacaps. As such, any pullback could be a great buying opportunity.
2. Nvidia
Nvidia (NVDA -0.42%) has once again been helping lead the market higher. The company recently got good news when the Trump administration said the U.S. would ease chip export controls, allowing the company to resume selling its H20 chips to China. This will add billions in revenue.
Nvidia remains the undisputed champion of AI infrastructure, with its graphics processing units (GPUs) the backbone of this build-out due to their fast processing speeds. And the company has sped up its development cycle to ensure it remains on top.
Over the past two years, data center revenue has exploded from $4.3 billion to more than $39 billion — incredible growth for a company the size of Nvidia. It held a 92% share in the GPU market in the first quarter.
Its chips drive sales, but its secret weapon is its CUDA software. The company created the free platform in 2006 as a way to expand the use of GPUs beyond their original purpose of speeding up graphics in video games.
While it was slow to play out in other end markets, Nvidia smartly pushed CUDA into academia and research labs, where early AI research was being done. That led developers to build directly on CUDA, leading to a growing collection of tools and libraries designed to maximize GPU performance for AI workloads.
If shares of Nvidia dip, be ready to pounce. Data center spending continues to ramp up, and the company has a big opportunity in the automotive market, too, as autonomous and smart vehicles start to become more prevalent.
3. Microsoft
Another company that has seen its stock run up in price is Microsoft (MSFT -0.32%). It dominates the enterprise software space with its Microsoft 365 suite of worker productivity products and has one of the leading cloud computing companies in Azure.
The cloud remains the company’s fastest-growing business, with the unit producing revenue growth of 30% or more each of the past seven quarters. Azure revenue jumped 33% last quarter (35% in constant currency), with nearly half of that coming from AI services. Growth could have been even higher, but Microsoft has been hitting capacity constraints.
As such, it plans to ramp up capital expenditures (capex) in fiscal 2026 with a focus on adding GPUs and servers. It said those assets are more directly tied to AI revenue than buying the buildings that house them. That’s a smart move that should support continued cloud computing momentum.
Microsoft’s $10 billion investment in OpenAI gave it an early AI lead, especially with Azure initially being granted exclusive access to its leading large language models (LLMs). Companies continue to be attracted to OpenAI’s popular AI models, and Azure gives its customers direct access to them.
It has also embedded OpenAI’s models throughout its ecosystem to run its Copilot, which is gaining popularity with businesses. At $30 per enterprise user per month, it offers a lot of strong upside.
That said, the OpenAI partnership is getting complicated. The exclusivity deal is over, and the two sides are reportedly negotiating new terms as the AI provider looks to restructure. Still, Microsoft remains entitled to 49% of OpenAI Global’s profits up to a tenfold return on its investment — potentially a huge payday.
Microsoft remains in a strong long-term position. The stock’s recent run-up makes it an attractive candidate to buy on any pullback.
Brand Stories
Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence
Lees, C. C. et al. ISUOG Practice Guidelines: diagnosis and management of small-for-gestational-age fetus and fetal growth restriction. Ultrasound Obstet. Gynecol. 56, 298–312 (2020).
Martin. Health—United Nations Sustainable Development. United Nations Sustainable Development https://www.un.org/sustainabledevelopment/health/#tab-3f22056b0e91266e8b2 (2023).
Krasevec, J. et al. Study protocol for UNICEF and WHO estimates of global, regional, and national low birthweight prevalence for 2000 to 2020. Gates Open Res. 6, 80 (2022).
Blencowe, H. et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob. Health 7, e849–e860 (2019).
Jia, C. H. et al. Short term outcomes of extremely low birth weight infants from a multicenter cohort study in Guangdong of China. Sci. Rep. 12, 11119 (2022).
Low birth weight. https://www.who.int/data/nutrition/nlis/info/low-birth-weight.
Okwaraji, Y. B. et al. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis. Lancet 403, 1071–1080 (2024).
International Institute for Population Sciences (IIPS) and Macro International. 2007. National Family Health Survey (NFHS-3), 2005–06: India: Volume I. Mumbai: IIPS.
Singh, D. et al. Prevalence and correlates of low birth weight in India: findings from National Family Health Survey 5. BMC Pregnancy Childbirth 23, 1–10 (2023).
Sabbaghchi, M., Jalali, R. & Mohammadi, M. A systematic review and meta-analysis on the prevalence of low birth weight infants in Iran. J. Pregnancy 2020, 1–7 (2020).
Institute of Medicine (US) Committee on Improving Birth Outcomes. In Improving Birth Outcomes: Meeting the Challenge in the Developing World (eds. Bale, J. R., Stoll, B. J., Lucas, A. O.) (National Academies Press, 2003). https://www.ncbi.nlm.nih.gov/books/NBK222095/.
Derakhshi, B., Esmailnasab, N., Ghaderi, E. & Hemmatpour, S. Risk factor of preterm labor in the west of Iran: a case-control study. DOAJ 43, 499–506 (2014).
Moradi, G., Zokaeii, M., Goodarzi, E. & Khazaei, Z. Maternal risk factors for low birth weight infants: a nested case-control study of rural areas in Kurdistan (western of Iran). J. Prev. Med. Hyg. (2021).
Katz, J. et al. Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis. Lancet 382, 417–425 (2013).
Knop, M. R. et al. Birth weight and risk of Type 2 diabetes mellitus, cardiovascular disease, and hypertension in adults: a meta-analysis of 7,646,267 participants from 135 studies. J. Am. Heart Assoc. 7, e008870 (2018).
Wu, M. et al. Estimation of fetal weight by ultrasonic examination. Int. J. Clin. Exp. Med. 8, 540–545 (2015).
Dwivedi, Y. K. et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 57, 101994 (2021).
Alanazi, A. Using machine learning for healthcare challenges and opportunities. Inform. Med. Unlocked 30, 100924 (2022).
Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, e214–e215 (2022).
Reza, T. B. & Salma, N. Prediction and feature selection of low birth weight using machine learning algorithms. J. Health Popul. Nutr. 43, 1–10 (2024).
Ranjbar, A. et al. Machine learning-based approach for predicting low birth weight. BMC Pregnancy Childbirth 23, 1–9 (2023).
de Morais, F. L. et al. Utilization of tree-based machine learning models for predicting low birth weight cases. BMC Pregnancy Childbirth 25, 207 (2025).
Naimi, A. I., Platt, R. W. & Larkin, J. C. Machine learning for fetal growth prediction. Epidemiology 29, 290 (2018).
Tao, J., Yuan, Z., Sun, L., Yu, K. & Zhang, Z. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med. Inform. Decis. Mak. 21, 1–10 (2021).
Rubaiya, N., Mansur, M., Alam, M. M. & Rayhan, M. I. Unraveling birth weight determinants: integrating machine learning, spatial analysis, and district-level mapping. Heliyon 10, e27341 (2024).
Sadeghi, Z. et al. A review of explainable artificial intelligence in healthcare. Comput. Electr. Eng. 118, 109370 (2024).
Gill, S. V., May-Benson, T. A., Teasdale, A. & Munsell, E. G. Birth and developmental correlates of birth weight in a sample of children with potential sensory processing disorder. BMC Pediatr. 13, 1–9 (2013).
Tarín, J. J., García-Pérez, M. A. & Cano, A. Potential risks to offspring of intrauterine exposure to maternal age-related obstetric complications. Reprod. Fertil. Dev. 29, 1468–1475 (2016).
Wang, S. et al. Changing trends of birth weight with maternal age: a cross-sectional study in Xi’an city of Northwestern China. BMC Pregnancy Childbirth 20, 1–7 (2020).
Softa, S. M. et al. The association of maternal height with mode of delivery and fetal birth weight at King Abdulaziz University Hosc, Jeddah,. Saudi Arabia. Cureus 14, e27493 (2022).
Rochow, N. et al. Maternal body height is a stronger predictor of birth weight than ethnicity: analysis of birth weight percentile charts. J. Perinat. Med. 47, 22–29 (2018).
Spada, E., Chiossi, G., Coscia, A., Monari, F. & Facchinetti, F. Effect of maternal age, height, BMI and ethnicity on birth weight: an Italian multicenter study. J. Perinat. Med. 46, 1016–1021 (2017).
Ludwig, D. S. & Currie, J. The association between pregnancy weight gain and birthweight: a within-family comparison. Lancet 376, 984–990 (2010).
Hussey, M. R. et al. Associations of placental lncRNA expression with maternal pre-pregnancy BMI and infant birthweight in two birth cohorts. J. Dev. Orig. Health Dis. 16, 1–10 (2025).
Garces, A. et al. Association of parity with birthweight and neonatal death in five sites: The Global Network’s Maternal Newborn Health Registry study. Reprod. Health 17, 1–8 (2020).
Shah, P. S. Parity and low birth weight and preterm birth: a systematic review and meta-analyses. Acta Obstet. Gynecol. Scand. 89, 862–875 (2010).
Wang, Y. A. et al. Preterm birth and low birth weight after assisted reproductive technology-related pregnancy in Australia between 1996 and 2000. Fertil. Steril. 83, 1650–1658 (2005).
Zhong, X. et al. Effect of parental physiological conditions and assisted reproductive technologies on the pregnancy and birth outcomes in infertile patients. Oncotarget 8, 18409–18416 (2016).
Rahmati, S., Azami, M., Badfar, G., Parizad, N. & Sayehmiri, K. The relationship between maternal anemia during pregnancy with preterm birth: a systematic review and meta-analysis. J. Matern. Fetal Neonatal Med. 33, 2679–2689 (2018).
Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A. & Sakharkar, M. K. Significance of HbA1C test in diagnosis and prognosis of diabetic patients. Biomark. Insights 11, BMI.S38440 (2016).
Gabbe, S. Management of diabetes mellitus complicating pregnancy. Obstet. Gynecol. 102, 857–868 (2003).
Li, J. et al. Maternal TSH levels at first trimester and subsequent spontaneous miscarriage: a nested case–control study. Endocr. Connect. 8, 1288–1293 (2019).
Nishioka, E. et al. Relationship between maternal thyroid-stimulating hormone (TSH) elevation during pregnancy and low birth weight: A longitudinal study of apparently healthy urban Japanese women at very low risk. Early Hum. Dev. 91, 181–185 (2015).
Bilardo, C. M. et al. ISUOG Practice Guidelines (updated): performance of 11–14-week ultrasound scan. Ultrasound Obstet. Gynecol. 61, 127–143 (2023).
Kalousová, M., Muravská, A. & Zima, T. Pregnancy-associated plasma protein A (PAPP-A) and preeclampsia. Adv. Clin. Chem. 63, 169–209 (2014).
Shiefa, S., Amargandhi, M., Bhupendra, J., Moulali, S. & Kristine, T. First trimester maternal serum screening using biochemical markers PAPP-A and free Β-HCG for Down syndrome, Patau syndrome and Edward syndrome. Indian J. Clin. Biochem. 28, 3–12 (2012).
Salomon, L. J. et al. ISUOG Practice Guidelines (updated): performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 59, 840–856 (2022).
Ogonowski, J. & Miazgowski, T. Intergenerational transmission of macrosomia in women with gestational diabetes and normal glucose tolerance. Eur. J. Obstet. Gynecol. Reprod. Biol. 195, 113–116 (2015).
Yang, Y. et al. The association of gestational diabetes mellitus with fetal birth weight. J. Diabetes Complications 32, 635–642 (2018).
Ardissino, M. et al. Maternal hypertension increases risk of preeclampsia and low fetal birthweight: Genetic evidence from a Mendelian randomization study. Hypertension 79, 588–598 (2022).
Cutland, C. L. et al. Low birth weight: case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine 35, 6492–6500 (2017).
Aziz, F., Khan, M. F. & Moiz, A. Gestational diabetes mellitus, hypertension, and dyslipidemia as the risk factors of preeclampsia. Sci. Rep. 14, 1–11 (2024).
Celik, N. Welch’s ANOVA: Heteroskedastic skew-t error terms. Commun. Stat. Theory Methods 51, 3065–3076 (2022).
Mujahid, M. et al. Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. J Big Data 11, 87 (2024).
Cerda, P., Varoquaux, G. & Kégl, B. Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107, 1477–1494 (2018).
Alkhawaldeh, I. M., Albalkhi, I. & Naswhan, A. J. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J. Methodol. 13, 373–378 (2023).
Abedin, M. Z., Guotai, C., Hajek, P. & Zhang, T. Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex Intell. Syst. 9, 3559–3579 (2022).
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual information. Phys. Rev. E 69, 066138 (2004).
Shipe, M. E., Deppen, S. A., Farjah, F. & Grogan, E. L. Developing prediction models for clinical use using logistic regression: an overview. J. Thorac. Dis. 11, S574–S584 (2019).
Montomoli, J. et al. Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients. J. Intensive Med. 1, 110–116 (2021).
Jabbar, M. A., Deekshatulu, B. L. & Chandra, P. Classification of heart disease using K-nearest neighbor and genetic algorithm. arXiv preprint arXiv:1508.02061 (2015).
Zheng, J. et al. Clinical data based XGBoost algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) multicenter retrospective case-control study. BMC Gastroenterol. 23, 1–13 (2023).
Loef, B. et al. Using random forest to identify longitudinal predictors of health in a 30-year cohort study. Sci. Rep. 12, 1–12 (2022).
Wallace, M. L. et al. Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction. BMC Med. Res. Methodol. 23, 1–12 (2023).
Singh, D. & Singh, B. Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97, 105524 (2019).
Kaliappan, J. et al. Impact of cross-validation on machine learning models for early detection of intrauterine fetal demise. Diagnostics (Basel) 13, 1692 (2023).
Elgeldawi, E., Sayed, A., Galal, A. R. & Zaki, A. M. Hyperparameter tuning for machine learning algorithms used for Arabic sentiment analysis. Informatics 8, 79 (2021).
Shekar, B. H. & Dagnew, G. Grid search-based hyperparameter tuning and classification of microarray cancer data. In Proc. Int. Conf. Adv. Comput. Commun. Paradigms (ICACCP) (2019). https://doi.org/10.1109/ICACCP.2019.8882943
Chadaga, K. et al. Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Sci. Rep. 14, 1–14 (2024).
Tougui, I., Jilbab, A. & Mhamdi, J. E. Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Healthc. Inform. Res. 27, 189–199 (2021).
Rodríguez-Pérez, R. & Bajorath, J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34, 1013–1026 (2020).
Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).
Abdullakutty, F. et al. Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis. Front. Med. 11, 1450103 (2024).
Javaid, M., Haleem, A., Pratap Singh, R., Suman, R. & Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022).
Arain, Z., Iliodromiti, S., Slabaugh, G., David, A. L. & Chowdhury, T. T. Machine learning and disease prediction in obstetrics. Curr. Res. Physiol. 6, 100099 (2023).
Alzubaidi, M. et al. Ensemble transfer learning for fetal head analysis: From segmentation to gestational age and weight prediction. Diagnostics 12, 2229 (2022).
Hughes, M. M., Black, R. E. & Katz, J. 2500-g low birth weight cutoff: history and implications for future research and policy. Matern. Child Health J. 21, 283 (2016).
Zeegers, B. et al. Birthweight charts customised for maternal height optimises the classification of small and large-for-gestational age newborns. Acta Paediatr. https://doi.org/10.1111/APA.17332 (2024).
Teoh, Z.H., Mariapun, J., Ko, V.S.Y., Dominic, N.A., Jeganathan, R., Karalasingam, S.D. & Thirunavuk Arasoo, V.J. Maternal height, and ethnicity and birth weight: A retrospective cohort study of uncomplicated term vaginal deliveries in Malaysia. Birth. 51, 620–628 (2024).
Sutan, R., Mohtar, M., Mahat, A. N. & Tamil, A. M. Determinant of low birth weight infants: a matched case control study. Open J. Prev. Med. 4, 91–99 (2014).
To, W. W. K., Cheung, W. & Kwok, J. S. Y. Paternal height and weight as determinants of birth weight in a Chinese population. Am. J. Perinatol. 15, 545–548 (1998).
Pölzlberger, E., Hartmann, B., Hafner, E., Stümpflein, I. & Kirchengast, S. Maternal height and pre-pregnancy weight status are associated with fetal growth patterns and newborn size. J. Biosoc. Sci. 49, 392–407 (2017).
Ay, L. et al. Maternal anthropometrics are associated with fetal size in different periods of pregnancy and at birth: the Generation R Study. BJOG 116, 953–963 (2009).
Mathai, M. et al. Ethnicity and fetal growth in Fiji. Aust. N. Z. J. Obstet. Gynaecol. 44, 318–321 (2004).
Figueras, F. et al. Customized birthweight standards for a Spanish population. Eur. J. Obstet. Gynecol. Reprod. Biol. 136, 20–24 (2008).
Britto, R. P. D. A. et al. Influence of maternal height and weight on low birth weight: a cross-sectional study in poor communities of northeastern Brazil. PLoS ONE 8, e000000 (2013).
Mongelli, M., Figueras, F., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for an Australian population. Aust. N. Z. J. Obstet. Gynaecol. 47, 128–131 (2007).
McCowan, L., Stewart, A. W., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for a New Zealand population. Aust. N. Z. J. Obstet. Gynaecol. 44, 428–431 (2004).
Zhang, G. et al. Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. 12, e100000 (2015).
Kaur, S. et al. Risk factors for low birth weight among rural and urban Malaysian women. BMC Public Health 19, 539 (2019).
Inoue, S. et al. Association between short maternal height and low birth weight: a hospital-based study in Japan. J. Korean Med. Sci. 31, 353–359 (2016).
Retnakaran, R. et al. Association of timing of weight gain in pregnancy with infant birth weight. JAMA Pediatr. 172, 136–142 (2018).
Mohapatra, I., Harshini, N., Samantaray, S. R. & Naik, G. Association between early pregnancy body mass index and gestational weight gain in relation to neonatal birth weight. Cureus 14, e23000 (2022).
Arora, P. & Tamber Aeri, B. Gestational weight gain among healthy pregnant women from Asia in comparison with Institute of Medicine (IOM) Guidelines-2009: a systematic review. J. Pregnancy 2019, 3849596 (2019).
Hackmon, R. et al. Do early fetal measurements and nuchal translucency correlate with term birth weight?. J. Obstet. Gynaecol. Can. 39, 750–756 (2017).
Jackson, M. & Rose, N. C. Diagnosis and management of fetal nuchal translucency. Semin. Roentgenol. 33, 333–338 (1998).
Kalem, Z., Kaya, A. E., Bakırarar, B. & Kalem, M. N. Fetal nuchal translucency: is there an association with birthweight and neonatal wellbeing?. Turk. J. Obstet. Gynecol. 16, 35–40 (2019).
Napolitano, R. et al. Pregnancy dating by fetal crown–rump length: a systematic review of charts. BJOG 121, 556–565 (2014).
Fries, N. et al. The impact of optimal dating on the assessment of fetal growth. BMC Pregnancy Childbirth 21, 1–8 (2021).
Salomon, L. J. Early fetal growth: concepts and pitfalls. Ultrasound Obstet. Gynecol. 35, 385–389 (2010).
Papageorghiou, A. T. et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown–rump length in the first trimester of pregnancy. Ultrasound Obstet. Gynecol. 44, 641 (2014).
Kozuki, N. et al. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: A meta-analysis. BMC Public Health 13(Suppl 3), S2 (2013).
Hinkle, S. N. et al. The association between parity and birthweight in a longitudinal consecutive pregnancy cohort. Paediatr. Perinat. Epidemiol. 28, 106 (2013).
Krulewitch, C. J., Herman, A. A., Yu, K. F. & Johnson, Y. R. Does changing paternity contribute to the risk of intrauterine growth retardation?. Paediatr. Perinat. Epidemiol. 11, 41–47 (1997).
Luo, Z. C. et al. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr. Perinat. Epidemiol. 21, 36–45 (2007).
Sørnes, T. & Bakke, T. Uterine size, parity and umbilical cord length. Acta Obstet. Gynecol. Scand. 68, 439–441 (1989).
Clapp, J. F. & Capeless, E. Cardiovascular function before, during, and after the first and subsequent pregnancies. Am. J. Cardiol. 80, 1469–1473 (1997).
Dasa, T. T., Okunlola, M. A. & Dessie, Y. Effect of grand multiparity on the adverse birth outcome: a hospital-based prospective cohort study in Sidama Region, Ethiopia. Int. J. Womens Health 14, 363 (2022).
Lin, L., Lu, C., Chen, W., Li, C. & Guo, V. Y. Parity and the risks of adverse birth outcomes: a retrospective study among Chinese. BMC Pregnancy Childbirth 21, 1–11 (2021).
Baer, R. J., Lyell, D. J., Norton, M. E., Currier, R. J. & Jelliffe-Pawlowski, L. L. First trimester pregnancy-associated plasma protein-A and birth weight. Eur. J. Obstet. Gynecol. Reprod. Biol. 198, 1–6 (2016).
Turrado Sánchez, E. M., De Miguel Sánchez, V. & Macía Cortiñas, M. Correlation between PAPP-A levels determined during the first trimester and birth weight at full-term. Reprod. Sci. 30, 3235 (2023).
Zhao, D. et al. Association between maternal blood glucose levels during pregnancy and birth outcomes: a birth cohort study. Int. J. Environ. Res. Public Health 20, 2102 (2023).
Everett, T. R. & Lees, C. C. Beyond the placental bed: placental and systemic determinants of the uterine artery Doppler waveform. Placenta 33, 893–901 (2012).
Liu, Y. et al. Impact of gestational hypertension and preeclampsia on low birthweight and small-for-gestational-age infants in China: a large prospective cohort study. J. Clin. Hypertens. 23, 835 (2021).
Gibbs, C. M., Wendt, A., Peters, S. & Hogue, C. J. The impact of early age at first childbirth on maternal and infant health. Paediatr. Perinat. Epidemiol. 26, 259–284 (2012).
Markovitz, B. P., Cook, R., Flick, L. H. & Leet, T. L. Socioeconomic factors and adolescent pregnancy outcomes: distinctions between neonatal and post-neonatal deaths?. BMC Public Health 5, 279 (2005).
Sharma, V. et al. Young maternal age and the risk of neonatal mortality in rural Nepal. Arch. Pediatr. Adolesc. Med. 162, 828–835 (2008).
Alam, N. Teenage motherhood and infant mortality in Bangladesh: maternal age-dependent effect of parity one. J. Biosoc. Sci. 32, 229–236 (2000).
Restrepo-Méndez, M. C. et al. Childbearing during adolescence and offspring mortality: findings from three population-based cohorts in southern Brazil. BMC Public Health 11, 781 (2011).
Paranjothy, S., Broughton, H., Adappa, R. & Fone, D. Teenage pregnancy: who suffers?. Arch. Dis. Child. 94, 239–245 (2009).
Lawlor, D. A., Mortensen, L. & Andersen, A. M. N. Mechanisms underlying the associations of maternal age with adverse perinatal outcomes: a sibling study of 264,695 Danish women and their firstborn offspring. Int. J. Epidemiol. 40, 1205–1214 (2011).
Fall, C. H. D. et al. Association between maternal age at childbirth and child and adult outcomes in the offspring: a prospective study in five low-income and middle-income countries (COHORTS collaboration). Lancet Glob. Health 3, e366–e377 (2015).
Alves, C., Jenkins, S. M. & Rapp, A. Early pregnancy loss (spontaneous abortion) (StatPearls, 2023).
Jackson, T. & Watkins, E. Early pregnancy loss. J. Am. Acad. Physician Assist. 34, 22–27 (2021).
Nicolaides, K. H. A model for a new pyramid of prenatal care based on the 11 to 13 weeks’ assessment. Prenat. Diagn. 31, 3–6 (2011).
Bekele, W. T. Machine learning algorithms for predicting low birth weight in Ethiopia. BMC Med. Inform. Decis. Mak. 22, 1–16 (2022).
Arayeshgari, M., Najafi-Ghobadi, S., Tarhsaz, H., Parami, S. & Tapak, L. Machine learning-based classifiers for the prediction of low birth weight. Healthc. Inform. Res. 29, 54 (2023).
Pollob, S. M. A. I., Abedin, M. M., Islam, M. T., Islam, M. M. & Maniruzzaman, M. Predicting risks of low birth weight in Bangladesh with machine learning. PLoS ONE 17, e000000 (2022).
Khan, W. et al. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Sci. Rep. 12, 1–12 (2022).
Hussain, Z. & Borah, M. D. Birth weight prediction of newborn baby with application of machine learning techniques on features of mother. J. Stat. Manag. Syst. 23, 1079–1091 (2020).
Alabbad, D. A. et al. Birthweight range prediction and classification: a machine learning-based sustainable approach. Mach. Learn. Knowl. Extr. 6, 770–788 (2024).
Dola, S. S. & Valderrama, C. E. Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset. BMC Med. Inform. Decis. Mak. 24, 367 (2024).
Liu, Q. et al. Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China. BMC Pregnancy Childbirth 25, 1–10 (2025).
-
Mergers & Acquisitions1 week agoAmazon weighs further investment in Anthropic to deepen AI alliance
-
Brand Stories2 weeks ago
Voice AI Startup ElevenLabs Plans to Add Hubs Around the World
-
Mergers & Acquisitions1 week ago
How Elon Musk’s rogue Grok chatbot became a cautionary AI tale
-
Mergers & Acquisitions1 week ago
UK crime agency arrests 4 people over cyber attacks on retailers
-
Asia Travel Pulse2 weeks ago
Looking For Adventure In Asia? Here Are 7 Epic Destinations You Need To Experience At Least Once – Zee News
-
AI in Travel2 weeks ago
‘Will AI take my job?’ A trip to a Beijing fortune-telling bar to see what lies ahead | China
-
Mergers & Acquisitions1 week ago
EU pushes ahead with AI code of practice
-
Mergers & Acquisitions2 weeks ago
ChatGPT — the last of the great romantics
-
Mergers & Acquisitions1 week ago
Humans must remain at the heart of the AI story
-
The Travel Revolution of Our Era1 month ago
CheQin.ai Redefines Hotel Booking with Zero-Commission Model
