Analysis and Prediction of Life Expectancy Using Machine Learning Methods Based on Behavioral and Lifestyle Factors

Abstract

Life expectancy is a critical indicator of public health, influenced by multiple factors including behavioral and lifestyle choices such as physical activity, diet, smoking, and alcohol consumption. This study aims to identify key risk factors that influence life expectancy and contribute to mortality changes. Using data from 2000 to 2021,utilizing statistical regression models and deep learning techniques (LSTM), a comparative analysis was conducted for Kyrgyzstan, Japan, and Kazakhstan, focusing on smoking, alcohol consumption, and Body Mass Index (BMI). The findings demonstrate significant impacts of these factors on life expectancy and mortality rates and reveal substantial differences between developed and developing coun- tries. This study highlights the importance of strategic public health measures and shows the potential for developing predictive models that can support personalized healthcare and help shape policies aimed at improving life expectancy.

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