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Title: USING RISK FACTORS FOR DISEASE TO PREDICT PROBABILITY OF CONTRACTING A DISEASE IN A MACHINE LEARNING-BASED PRODUCT THAT RECOMMENDS INTERVENTIONS TO INCREASE HEALTH AND LONGEVITY
Authors: John Leddo
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John Leddo MyEdMaster, LLC
MLA 8 Leddo, John. "USING RISK FACTORS FOR DISEASE TO PREDICT PROBABILITY OF CONTRACTING A DISEASE IN A MACHINE LEARNING-BASED PRODUCT THAT RECOMMENDS INTERVENTIONS TO INCREASE HEALTH AND LONGEVITY." Int. j. of Social Science and Economic Research, vol. 8, no. 9, Sept. 2023, pp. 3021-3028, doi.org/10.46609/IJSSER.2023.v08i09.041. Accessed Sept. 2023. APA 6 Leddo, J. (2023, September). USING RISK FACTORS FOR DISEASE TO PREDICT PROBABILITY OF CONTRACTING A DISEASE IN A MACHINE LEARNING-BASED PRODUCT THAT RECOMMENDS INTERVENTIONS TO INCREASE HEALTH AND LONGEVITY. Int. j. of Social Science and Economic Research, 8(9), 3021-3028. Retrieved from https://doi.org/10.46609/IJSSER.2023.v08i09.041 Chicago Leddo, John. "USING RISK FACTORS FOR DISEASE TO PREDICT PROBABILITY OF CONTRACTING A DISEASE IN A MACHINE LEARNING-BASED PRODUCT THAT RECOMMENDS INTERVENTIONS TO INCREASE HEALTH AND LONGEVITY." Int. j. of Social Science and Economic Research 8, no. 9 (September 2023), 3021-3028. Accessed September, 2023. https://doi.org/10.46609/IJSSER.2023.v08i09.041.
References [1]. Bahrampour, S., Ramirez, L., Azimi, J., & Davidson, N. (2019). Interpretability in Machine Learning: An Overview of Transparency and Explainability in AI. arXiv preprint arXiv:1903.03894. [2]. Bhadra, A., Saha, S., & Singh, D. (2019). Prediction of Type 2 Diabetes using Machine Learning Algorithms. Journal of Health and Medical Informatics, 10(1), 1-9. [3]. Bishop,C. M. &Tipping,M.E.(2003).Bayesian regression and classification. Nato Science Series sub Series III Computer And Systems Sciences,190:267-288. [4]. Chen, M., Zhou, X., He, T., & Huang, Z. (2020). Federated Learning in Healthcare: A Review and Case Studies. arXiv preprint arXiv:2007.07835. [5]. Drew, B. J., & Reid, C. L. (2019). Early Detection of Cancer: Evaluation of a Machine Learning Model using Clinical Notes in Electronic Health Records. Journal of Oncology Practice, 15(6), e531-e538. [6]. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial Intelligence in Radiology. Nature Reviews Cancer, 18(8), 500-510. [7]. Jain, H., Redrouthu, S., Agarwal, J., Agarwal, T., Leddo, J. et al. (2023). A Machine Learning-based Lifespan Calculator. International Journal of Social Science and Economic Research, 8(7), 2102-2108. [8]. Lu, T., Yuan, Y., Agarwal, J., Agarwal, T., Jain, H., Leddo, J. et al. (2023). A Meta-regression and Bayesian Regression Framework for Combining Results of Scientific Research and Surveys of People’s Lifestyles to Make Recommendations on What Interventions Will Help Them Live Longer and Healthier. International Journal of Social Science and Economic Research, 8(3), 524-531. [9]. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2019). Ensuring Fairness in Machine Learning to Advance Health Equity. Annals of Internal Medicine, 170(10), 681-682. [10]. van Houwelingen,H.C., Arends,L.R. &Stijnen,T.(2002). Advanced methods in meta-analysis: multivariate approach and meta-regression. Statistics in Medicine,21(4):589-624. doi:10.1002/sim.1040 [11]. Whelton, P. K., Carey, R. M., Aronow, W. S., et al. (2018). 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension, 71(6), e13-e115. [12]. Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H. & Kannel, W.B. (1998). Prediction of coronary heart disease using risk factor categories. Circulation. 97(18):1837-1847.
ABSTRACT: In previous papers, we have described a methodology of using meta-regression and Bayesian statistics to create a machine learning model that combines scientific research on wellness and longevity and responses from people’s lifestyle questionnaires to make recommendations on what people can do to live longer and healthier. One of the goals of this software is to use data collected from logs of people’s lifestyle choices to update the model and increasingly improve and personalize the recommendations made. One challenge faced here is that the elapsed time between lifestyle choices and the onset of disease may take years, making it difficult to make timely recommendations based on the effectiveness of what people are implementing in their daily lives. One way to address this challenge is to measure risk factors for disease rather than occurrence of disease itself as properly-selected risk factors may be more sensitive to changes in lifestyle choices while still being highly predictive of the risk of contracting major diseases. The present paper provides a methodology of using such risk factors in a machine learning model to recommend interventions for what people can do to live longer and healthier lives.

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