Chronic Diseases: A Comprehensive Review of Applications for Prediction of Obesity

Obesity is a multifaceted public health crisis, affecting millions globally and serving as a precursor to a myriad of serious health conditions such as diabetes, cardiovascular diseases, and certain cancers [1-3]. Despite extensive efforts to mitigate its impact through public health policies, medical interventions, and individual lifestyle changes, the prevalence of obesity continues to rise [4,5]. This persistent challenge underscores the need for innovative approaches that transcend traditional methodologies. Machine Learning (ML), with its ability to harness large volumes of diverse data to uncover patterns and insights beyond human discernment, presents a promising frontier in the battle against obesity [6-8].

Recent advancements in ML have opened new avenues for addressing complex health issues by facilitating personalized medicine, predictive diagnostics, and behaviour modification strategies. In the context of obesity, ML algorithms can analyse vast datasets-from genetic predispositions to behavioural and environmental factors-enabling the development of tailored intervention strategies that are more adaptive and responsive to individual needs [9,10]. Moreover, ML can enhance the real-time monitoring and management of obesity through wearable technology and mobile applications, offering immediate feedback and support to individuals as they navigate their daily choices [11,12]. The integration of ML into obesity research and management is not without challenges [13,14]. Issues such as data privacy, algorithmic bias, and the digital divide pose significant barriers to the widespread adoption of these technologies [7,15]. Moreover, the effectiveness of ML-driven interventions must be scrutinized through rigorous, multidisciplinary research to ensure they deliver practical health outcomes without exacerbating existing inequalities [9,11,16,17]. This review paper aims to critically explore how ML is being applied within the field of obesity management. By examining the current landscape, addressing the challenges, and discussing future directions, this paper seeks to highlight the transformative potential of ML technologies in crafting more effective, personalized, and sustainable solutions for obesity.

Search strategy

The literature search for this review was conducted across several databases including PubMed, IEEE Xplore, ScienceDirect, and Google Scholar, focusing on publications from January 2010 to December 2023. We used keywords such as “machine learning”, “artificial intelligence”, “obesity”, “weight management”, “predictive modelling”, “personalized medicine”, and “digital health technologies”. Our search was limited to studies published in English and included both peer-reviewed articles and significant conference proceedings to encompass a comprehensive scope of the advancements in machine learning applications in obesity management.

Selection criteria

The selection of studies was based on their relevance to the implementation of machine learning in the realm of obesity. We included studies that applied machine learning to obesity assessment, prediction, and management, and those evaluating the efficacy of ML-based interventions. Reviews and meta-analyses related to the application of ML in dietary monitoring, physical activity, or obesity were also considered. Exclusion criteria encompassed studies not primarily focused on obesity, nonempirical pieces like opinions or editorials, and research lacking detailed methodology or outcome data.

Data extraction

For each selected study, pertinent details such as the authors, publication year, objectives, ML techniques used, data sources, sample size, principal findings, and noted limitations were meticulously extracted. This data provided a foundation for synthesizing and understanding the extent and impact of machine learning applications in obesity management.

Quality assessment

The quality of the studies was assessed using criteria adapted from the Critical Appraisal Skills Programme (CASP) and the STROBE guidelines. Important quality metrics included the clarity of study objectives, the appropriateness of the ML techniques employed, the robustness of the data analysis, and the validity of the conclusions drawn.

Data synthesis

We employed a narrative synthesis approach, allowing for a thematic analysis of the various machine learning applications within obesity management, categorized by predictive modelling, behavioural interventions, and clinical decision support systems.

This analysis helped identify common themes, compare results across different studies, and pinpoint gaps in the research landscape.

Ethical considerations

Although our review did not involve primary data collection and thus did not require ethical approval, it integrated a discussion on the ethical aspects of AI, including data privacy and security. This reflection aimed to underscore how the selected studies addressed ethical concerns.

Limitations of the review methodology

We acknowledge certain limitations in our review approach, such as potential publication bias and language bias due to the exclusion of non-English publications. Additionally, the variable quality of the studies included could affect the reliability of our conclusions. These factors were considered in the interpretation of the findings and are critical for understanding the scope and implications of our review. This paragraph-based methodology ensures a thorough and systematic exploration of the latest machine learning strategies in the field of obesity management, setting the stage for future research directions and innovations.

Obesity is one of the most pressing health crises of the 21st century, acting as a major risk factor for numerous chronic diseases, including diabetes, heart disease, and certain types of cancer [18-21]. The global prevalence of obesity has nearly tripled since 1975, making it a critical public health challenge that not only diminishes quality of life but also imposes substantial economic burdens on healthcare systems worldwide [19-24]. Traditional methods of management, such as dietary guidance and physical activity promotion, often fall short in both adherence and longterm effectiveness [25,26]. This scenario underscores the urgent need for innovative approaches in predicting and managing obesity [27-30]. The necessity for new predictive methods in combating obesity is driven by the need to tailor interventions to individual characteristics and circumstances [31-35]. Current approaches typically employ one-size-fits-all strategies that do not account for the complex interplay of genetic, environmental, and personal factors that influence obesity [36,37]. Machine Learning (ML) and other advanced predictive technologies offer promising tools to fill this gap [38,39]. These technologies can analyse vast arrays of data, from genetic profiles to lifestyle habits, providing personalized insights that can guide more effective intervention strategies [39,40].

For instance, ML can predict which individuals are at higher risk of obesity or its related complications based on their genetic makeup, behaviour patterns, and even social determinants of health [21,26,41,42]. This allows for earlier and more targeted interventions, potentially preventing the onset of obesity rather than merely attempting to reverse it [28,30,43-45]. Furthermore, predictive analytics can help monitor the effectiveness of prescribed interventions in real-time, allowing for adjustments that improve outcomes [25,39,46,47]. Moreover, as obesity continues to be a significant predictor of severe outcomes in other diseases, such as COVID-19, the role of predictive methods becomes even more crucial [48,49]. The ability to integrate diverse data types and predict outcomes on an individual level could transform the landscape of public health by enabling more proactive, personalized, and effective obesity management strategies, ultimately reducing the global burden of this critical condition [34,37,50].

Machine learning (ML) has significantly transformed the landscape of health informatics, particularly in addressing complex health conditions like obesity [31,51]. Obesity, characterized by excessive fat accumulation, poses serious health risks and challenges, but ML offers innovative solutions for both its understanding and management [32,52]. By integrating ML into obesity research, scientists and healthcare providers can analyse large, diverse data sets to identify patterns and predictors of obesity that are not immediately apparent through conventional statistical methods [40,46]. One of the primary applications of ML in the context of obesity is in the development of predictive models [5,13]. These models utilize a variety of inputs, including genetic, dietary, and physical activity data, to forecast individual risk factors and the potential success of specific interventions [53,54]. For example, ML algorithms can sift through complex dietary intake data and physical activity logs to personalize diet and exercise plans that are more likely to be effective for specific individuals [55,56].

Beyond prediction, ML is also being used to enhance behavioural modification programs [57]. Through the analysis of real-time data from wearable devices and mobile health applications, ML algorithms can provide immediate feedback and personalized recommendations, encouraging healthier lifestyle choices [58,59]. This real-time monitoring and adjustment can significantly improve adherence to diet and exercise programs, which is often a major hurdle in traditional obesity management strategies [60,61]. Additionally, ML helps in segmenting populations based on their risk and response to different treatments, which can lead to more targeted and effective public health interventions [7,62]. This capability is particularly important in managing obesity at the population level, where one-size-fits-all approaches have often failed [63]. Despite these advancements, the application of ML in obesity management is not without challenges. Issues such as data privacy, the need for large and diverse datasets to train algorithms effectively, and the potential for bias in algorithmic decisions must be carefully managed [64,65]. However, with ongoing advancements in technology and more rigorous data handling practices, ML continues to hold promise for revolutionizing the fight against obesity. The integration of Machine Learning (ML) in addressing obesity is an evolving field with significant potential for future development. As we continue to amass large datasets and refine algorithmic techniques, the scope for ML to revolutionize obesity management and prevention is immense. Future avenues for research and application are likely to focus on several key areas that enhance both the precision and effectiveness of interventions [66,67].

Firstly, the development of more sophisticated predictive models stands as a critical future avenue. These models will increasingly utilize complex datasets that include not just medical and genetic information, but also detailed behavioural, environmental, and social data. By harnessing the power of big data analytics, ML can provide deeper insights into the multifactorial causes of obesity, predicting individual susceptibility with higher accuracy. This precision will allow for the implementation of preemptive measures tailored to individual risk profiles before obesity develops [66-69]. Secondly, personalized treatment plans based on ML predictions are set to become more nuanced. As algorithms become better at processing diverse data types-from dietary intake and physical activity to sleep patterns and psychosocial factors-they will offer more customized recommendations. These plans will not only suggest specific dietary and exercise regimes but also integrate behaviour modification strategies, potentially including Cognitive Behavioural Therapy (CBT) elements, tailored to individual motivational factors and barriers [9,70,71].

Another promising direction is the integration of ML with wearable technology and IoT (Internet of Things) devices to provide real-time, continuous monitoring and feedback. This technology could dynamically adjust recommendations based on daily activity levels and physiological responses, thus maintaining optimal engagement and efficacy of interventions [72]. In summary, the future of ML in combating obesity looks toward not only advancing predictive capabilities but also enhancing personalized interventions and real-time adaptive systems, all while navigating the complex ethical landscape of health data utilization.

In conclusion, the intersection of Machine Learning (ML) and obesity management represents a promising frontier in addressing a pervasive global health crisis. Through comprehensive literature reviews and innovative methodologies, it becomes clear that ML has the potential to transform obesity interventions by enabling personalized, predictive, and real-time approaches. Despite challenges such as data privacy and the need for diverse, highquality datasets, the future holds significant promise for ML to provide tailored solutions that enhance individual health outcomes. Continued collaboration across disciplines and thoughtful consideration of ethical implications will be key to harnessing the full potential of ML in combating obesity.

1. Torres-Martos A, Bustos-Aibar M, Ramirez-Mena A, Camara-Sanchez S, Anguita-Ruiz A, et al. (2023) Omics data preprocessing for machine learning: A case study in childhood obesity. Genes (Basel) 14(2): 248.
2. Squillario M, Bonaretti C, La Valle A, Di Marco E, Piccolo G, et al. (2023) Gut-microbiota in children and adolescents with obesity: inferred functional analysis and machine-learning algorithms to classify microorganisms. Sci Rep 13(1): 11294.
3. Shin SH, Hur G, Kim NR, Park JHY, Lee KW, et al. (2023) A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor. Food Funct 14(4): 1869-1883.
4. Osadchiy V, Bal R, Mayer EA, Kunapuli R, Dong T, et al. (2023) Machine learning model to predict obesity using gut metabolite and brain microstructure data. Sci Rep 13(1): 5488.
5. Moroni A, Perna S, Azzolino D, Gasparri C, Zupo R, et al. (2023) Discovering the individualized factors associated with sarcopenia and sarcopenic obesity phenotypes-a machine learning approach. Nutrients 15(21): 4536.
6. Liu J, Zhang J, Zhao X, Pan C, Liu Y, et al. (2023) Identification of CXCL16 as a diagnostic biomarker for obesity and intervertebral disc degeneration based on machine learning. Sci Rep 13(1): 21316.
7. Lin W, Shi S, Huang H, Wen J, Chen G (2023) Predicting risk of obesity in overweight adults using interpretable machine learning algorithms. Front Endocrinol (Lausanne) 14: 1292167.
8. Kolli R, Razzaghi T, Pierce S, Edwards RK, Maxted M, et al. (2023) Predicting cesarean delivery among gravidas with morbid obesity-a machine learning approach. AJOG Glob Rep 3(4): 100276.
9. Khalid SI, Massaad E, Kiapour A, Bridge CP, Rigney G, et al. (2024) Machine learning-based detection of sarcopenic obesity and association with adverse outcomes in patients undergoing surgical treatment for spinal metastases. J Neurosurg Spine 40(3): 291-300.
10. Jeon S, Kim M, Yoon J, Lee S, Youm S (2023) Machine learning-based obesity classification considering 3D body scanner measurements. Sci Rep 13(1): 3299.
11. Jeon J, Lee S, Oh C (2022) Age-specific risk factors for the prediction of obesity using a machine learning approach. Front Public Health 10: 998782.
12. Gulu M, Yagin FH, Yapici H, Irandoust K, Dogan AA, et al. (2023) Is early or late biological maturation trigger obesity? A machine learning modeling research in turkey boys and girls. Front Nutr 10: 1139179.
13. Nudel J, Kenzik KM, Rajendran I, Hofman M, Srinivasan J, et al. (2023) A machine learning framework for optimizing obesity care by simulating clinical trajectories and targeted interventions. Obesity (Silver Spring) 31(11): 2665-2675.
14. Mondal PK, Foysal KH, Norman BA, Gittner LS (2023) Predicting childhood obesity based on single and multiple well-child visit data using machine learning classifiers. Sensors (Basel) 23(2): 759.
15. Lim H, Lee H, Kim J (2023) A prediction model for childhood obesity risk using the machine learning method: A panel study on Korean children. Sci Rep 13(1): 10122.
16. Guleken Z, Ceylan Z, Cecen S, Jakubczyk D, Jakubczyk P, et al. (2023) Chemical changes in childhood obesity blood as a marker of the disease. A Raman-based machine learning study. J Pharm Biomed Anal 233: 115445.
17. Ferreras A, Sumalla-Cano S, Martinez-Licort R, Elio I, Tutusaus K, et al. (2023) Correction to: systematic review of machine learning applied to the prediction of obesity and overweight. J Med Syst 47(1): 27.
18. Benitez-Andrades JA, Prada-Garcia C, Garcia-Fernandez R, Ballesteros-Pomar MD, Gonzalez-Alonso MI, et al. (2024) Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: A comprehensive study. Digit Health 10: 20552076241239274.
19. Azmi MB, Sehgal SA, Asif U, Musani S, Abedin MFE, et al. (2023) Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences. J Biomol Struct Dyn, pp. 1-17.
20. Allen B (2023) An interpretable machine learning model of cross-sectional U S county-level obesity prevalence using explainable artificial intelligence. PLoS One 18(10): e0292341.
21. Al-Zubayer MA, Alam K, Shanto HH, Maniruzzaman M, Majumder UK, et al. (2024) Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh. J Biosoc Sci 56(3): 426-444.
22. Boncan DAT, Yu Y, Zhang M, Lian J, Vardhanabhuti V (2024) Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study. NPJ Aging 10(1): 4.
23. Ben-Aharon I, Rotem R, Melzer-Cohen C, Twig G, Cercek A, et al. (2023) Pharmaceutical agents as potential drivers in the development of early-onset colorectal cancer: case-control study. JMIR Public Health Surveill 9: e50110.
24. Aravindraja C, Jeepipalli S, Duncan W, Vekariya KM, Bahadekar S, et al. (2023) Unique miromics expression profiles in tannerella forsythia-infected mandibles during periodontitis using machine learning. Int J Mol Sci 24(22): 16393.
25. Belykh A, Hawro I, Kolczynska-Matysiak K, Loza-Valdes A, Mieczkowski A, et al. (2024) Triazolo[4,5-d] pyrimidin-5-amines based ERK3 inhibitors fail to demonstrate selective effects on adipocyte function. Arch Biochem Biophys 751: 109825.
26. Bai J, Zhu L, Mi W, Gao Z, Ouyang M, et al. (2023) Multiscale integrative analyses unveil immune-related diagnostic signature for the progression of MASLD. Hepatol Commun 7(11): e0298.
27. Huang AA, Huang SY (2023) Computation of the distribution of model accuracy statistics in machine learning: comparison between analytically derived distributions and simulation-based methods. Health Sci Rep 6(4): e1214.
28. Huang AA, Huang SY (2023) Quantification of the relationship of pyridoxine and spirometry measurements in the United States population. Curr Dev Nutr 7(8): 100078.
29. Huang AA, Huang SY (2023) Increased vigorous exercise and decreased sedentary activities are associated with decreased depressive symptoms in United States adults: Analysis of The National Health and Nutrition Examination Survey (NHANES) 2017-2020. Health Sci Rep 6(8): e1473.
30. Huang AA, Huang SY (2023) Technical report: machine-learning pipeline for medical research and quality-improvement initiatives. Cureus 15(10): e46549.
31. Wong J, Li X, Arterburn DE, Li D, Messenger-Jones E, et al. (2024) Using claims data to predict pre-operative BMI among bariatric surgery patients: development of the BMI before bariatric surgery scoring system (B3S3). Pragmat Obs Res 15: 65-78.
32. West-Szymanski DC, Zhang Z, Cui XL, Kowitwanich K, Gao L, et al. (2024) Machine learning identifies cell-free DNA 5-hydroxymethylation biomarkers that detect occult colorectal cancer in PLCO Screening Trial subjects. bioRxiv.
33. Vannucci M, Niyishaka P, Collins T, Rodriguez-Luna MR, Mascagni P, et al. (2024) Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: A multicentre study. Surg Endosc 38(1): 229-239.
34. Turchin A, Morrison FJ, Shubina M, Lipkovich I, Shinde S, et al. (2024) EXIST: EXamining rIsk of excesS adiposiTy-Machine learning to predict obesity-related complications. Obes Sci Pract 10(1): e707.
35. Graffy P, Zimmerman L, Luo Y, Yu J, Choi Y, et al. (2024) Longitudinal clustering of life's essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study. J Am Med Inform Assoc 31(2): 406-415.
36. Vahid F, Dessenne C, Tur JA, Bouzas C, Devaux Y, et al. (2024) Multicomponent (bio) markers for obesity risk prediction: A scoping review protocol. BMJ Open.14(3): e083558.
37. Thomas DM, Knight R, Gilbert JA, Cornelis MC, Gantz MG, et al. (2024) Transforming big data into ai-ready data for nutrition and obesity research. Obesity (Silver Spring) 32(5): 857-870.
38. Verma N, Duseja A, Mehta M, De A, Lin H, et al. (2024) Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study. Aliment Pharmacol Ther 59(6): 774-788.
39. Tohidinezhad F, Zegers CML, Vaassen F, Dijkstra J, Anten M, et al. (2024) Predicting the risk of neurocognitive decline after brain irradiation in adult patients with a primary brain tumor. Neuro Oncol, p. noae035.
40. Wang H, Lv X, Zhao S, Yuan W, Zhou Q, et al. (2023) Weight loss promotion in individuals with obesity through gut microbiota alterations with a multiphase modified ketogenic diet. Nutrients 15(19): 4163.
41. Alnajar A, Benck KN, Dar T, Hirji SA, Ibrahim W, et al. (2023) Predictors of outcomes in patients with obesity following mitral valve surgery. JTCVS Open 15: 127-150.
42. Adhikari S, Mukhyopadhyay A, Kolzoff S, Li X, Nadel T, et al. (2023) Cohort profile: A large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence. BMJ Open 13(12): e076812.
43. Huang AA, Huang SY (2023) Quantification of the effect of vitamin e intake on depressive symptoms in United States adults using restricted cubic splines. Curr Dev Nutr 7(2): 100038.
44. Huang AA, Huang SY (2023) Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database. PLoS One 18(7): e0288819.
45. Huang AA, Huang SY (2023) Covariate dependent Markov chains constructed with gradient boost modelling can effectively generate long-term predictions of obesity trends. BMC Res Notes 16(1): 346.
46. Wang X, Qiao Y, Cui Y, Ren H, Zhao Y, et al. (2023) An explainable artificial intelligence framework for risk prediction of COPD in smokers. BMC Public Health 23(1): 2164.
47. Turhan S, Canbek U, Dubektas-Canbek T, Dogu E (2023) Predicting prolonged wound drainage after hemiarthroplasty for Hip fractures: A stacked machine learning study. Clin Orthop Surg 15(6): 894-901.
48. Tang J, Xu H, Xin Z, Mei Q, Gao M, et al. (2024) Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data. Hum Mol Genet 33(8): 733-738.
49. Shah AA, Devana SK, Lee C, SooHoo NF (2024) A predictive algorithm for perioperative complications and readmission after ankle arthrodesis. Eur J Orthop Surg Traumatol 34(3): 1373-1379.
50. Tao P, Conarello S, Wyche TP, Zhang NR, Chng K, et al. (2024) Metabolomics and lipidomics analyses aid model classification of type 2 diabetes in non-human primates. Metabolites 14(3): 159.
51. Wilensky A, Frank N, Mizraji G, Tzur D, Goldstein C, et al. (2023) Periodontitis and metabolic syndrome: statistical and machine learning analytics of a nationwide study. Bioengineering (Basel) 10(12): 1384.
52. Wang Y, Jin Z, Huang J, Lu H, Wang W (2023) Facial landmark based BMI analysis for pervasive health informatics. Annu Int Conf IEEE Eng Med Biol Soc, pp. 1-5.
53. Monshizadeh M, Ye Y (2024) Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction. Gut Microbes 16(1): 2302076.
54. Mohseni-Takalloo S, Mohseni H, Mozaffari-Khosravi H, Mirzaei M, Hosseinzadeh M (2024) The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest. BMC Bioinformatics 25(1): 18.
55. Mohseni M, Zargarzadeh S, Arjmand N (2024) Multi-task artificial neural networks and their extrapolation capabilities to predict full-body 3D human posture during one- and two-handed load-handling activities. J Biomech 162: 111884.
56. Minot SS, Garb B, Roldan A, Tang AS, Oskotsky TT, et al. (2023) MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies. Cell Rep Methods 3(11): 100639.
57. Midya V, Lane JM, Gennings C, Torres-Olascoaga LA, Gregory JK, et al. (2023) Prenatal lead exposure is associated with reduced abundance of beneficial gut microbial cliques in late childhood: An investigation using microbial co-occurrence analysis (MiCA). Environ Sci Technol 57(44): 16800-16810.
58. McDeed AP, Van Dyk K, Zhou X, Zhai W, Ahles TA, et al. (2024) Prediction of cognitive decline in older breast cancer survivors: The thinking and living with cancer study. JNCI Cancer Spectr 8(2): pkae019.
59. Lyu C, Joehanes R, Huan T, Levy D, Li Y, et al. (2024) Enhancing selection of alcohol consumption associated genes by random forest. Br J Nutr, pp. 1-10.
60. Luo Y, Xiao M, Chen X, Zeng W, Chen H (2024) Harsh, unpredictable childhood environments are associated with inferior frontal gyrus connectivity and binge eating tendencies in late adolescents. Appetite 195: 107210.
61. Luo J, Luo M, Kaminga AC, Wei J, Dai W, et al. (2024) Integrative metabolomics highlights gut microbiota metabolites as novel NAFLD-related candidate biomarkers in children. Microbiol Spectr 12(4): e0523022.
62. Lim H, Lee H (2024) Eating habits and lifestyle factors related to childhood obesity among children aged 5-6 years: cluster analysis of panel survey data in korea. JMIR Public Health Surveill 10: e51581.
63. Li Y, Sun X, Wang M, Jiang Y, Ge QQ, et al. (2024) Meta-analysis and machine learning reveal the antiobesity effects of melatonin on obese rodents. Obes Rev 25(5): e13701.
64. Li Q, Tang X, Li W (2023) Potential diagnostic markers and biological mechanism for osteoarthritis with obesity based on bioinformatics analysis. PLoS One 18(12): e0296033.
65. Li C, Liu X, Shen P, Sun Y, Zhou T, et al. (2024) Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. Eur Heart J Digit Health 5(1): 30-40.
66. Lee H, Yoo G, Pak D, Lee JH (2024) Evaluation of D-dimer and prothrombin time in alcohol related liver cirrhosis with comparison of machine learning analyses. Int J Med Inform 186: 105407.
67. Kupek E, Liberali R (2024) Food patterns associated with overweight in 7-11-year old children: machine-learning approach. Cien Saude Colet 29(1): e14712022.
68. Dimitri P, Savage MO (2024) Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 37(3): 209-221.
69. Delfin C, Dragan I, Kuznetsov D, Tajes JF, Smit F, et al. (2024) A federated database for obesity research: an IMI-SOPHIA study. Life (Basel) 14(2): 262.
70. Kour B, Shukla N, Bhargava H, Sharma D, Sharma A, et al. (2023) Identification of plausible candidates in prostate cancer using integrated machine learning approaches. Curr Genomics 24(5): 287-306.
71. Jiang L, Yang Z, Liu G, Xia Z, Yang G, et al. (2024) A feature optimization study based on a diabetes risk questionnaire. Front Public Health 12: 1328353.
72. Jhinjharia D, Kaushik AC, Sahi S (2023) A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy. J Biomol Struct Dyn, pp. 1-21.