Application of Machine Learning in Identifying Premature Aging

Madina Suleimenova, Kuat Abzaliyev, Akbota Bugibayeva*, Symbat Abzaliyeva, Diana Sundetova, Almagul Kurmanova, Absatarova Karashash, Ardak Nurbakyt, Jardemova K, Merey Abdykassymova, Raziya Ospanova, Nazipa Baizhigitova, Ulzhas Sagalbayeva and Raushan Bitemirova

Previously pathological ageing is known to be mainly associated with the development of cardiovascular disease, which is caused by a combination of socioeconomic, metabolic, behavioural, and environmental risk factors. These include sex differences, age, high blood pressure, obesity, low physical activity, smoking, heavy alcohol consumption, high cholesterol and glucose levels, and diabetes mellitus. All processes occurring in the human body are reflected in biochemical and immunological analyses, which may be markers of early cardiovascular aging. The authors investigated correlations between social factors such as education, work, gender, family life, disability, physical activity, smoking and alcohol, and clinical findings such as coronary heart disease (CHD), postinfarct cardiosclerosis (PICS), chronic heart failure (CHF), diabetes mellitus (DM), body mass index (BMI), glucose levels, total cholesterol (TC) and blood pressure (BP) in older individuals. The authors implemented the basic model using R programming language, RStudio version 2023.09.0+463 on a device running MacBook Air M1, 8 GB RAM to identify the association between clinical data and social life. Also, two mathematical models (1)-(3) were proposed to calculate the correlation in combination with traditional machine learning classifiers between clinical data (biomarkers) and the social life of a patient in the age group of 60-74 years, 75-89 years and 90 and above.

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Published on: Aug 13, 2024 Pages: 13-21

Full Text PDF Full Text HTML DOI: 10.17352/aggr.000038
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