[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123238-en":3,"doc-seo-123238-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123238,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","The Impact of Socio-Health Factors Towards Life Expectancy across Countries using Machine Learning","Life expectancy functions as a key indicator of population well-being, shaped by multiple socio-health determinants that vary across countries. Identifying the drivers behind these differences supports the design of public health policies and targeted interventions to improve population health outcomes. With machine learning enabling data-driven prediction, the study investigates socio-health factors influencing life expectancy across multiple national contexts. Findings highlight age group, immunisation status, and disease presence such as HIV/AIDS as significant predictors.","The Impact of Socio-Health Factors Towards Life Expectancy across Countries using  \nMachine Learning  \nMohamad Hafizuddin Roslan1, Siti Nur Kamariah AbRasid2, Alfi Tristan Al-Tavip3, Nurzulaikha Abdullah4* and  \nFakhitah Ridzuan5  \n1Faculty of Data Science and Computing, Universiti Malaysia Kelantan, 16100, Kota Bharu, Kelantan  \n2Fakultas Ilmu Komputeran, Mercu Buana University, Jl. Meruya Selatan No. 1 Kembangan,11650 Jakarta Barat, Indonesia  \nAuthors’email: [s21a0025@siswa.umk.edu.my](s21a0025@siswa.umk.edu.my), [s21a0055@siswa.umk.edu.my](s21a0055@siswa.umk.edu.my), [s23m0260@siswa.umk.edu.my](s23m0260@siswa.umk.edu.my),  \n[nurzulaikha.mal@umk.edu.my](nurzulaikha.mal@umk.edu.my)* [and fakhitah.r@umk.edu.my](and fakhitah.r@umk.edu.my)  \n*Corresponding author  \nReceived 25 October 2024; Received in revised 26 November 2024; Accepted 10 December 2024 Available online 15 December 2024  \nAbstract: Life expectancy is a critical indicator of societal well-being and quality of life. Discovering and discussing the numerous factors that contribute to variations in life expectancy is crucial.  \nUnderstanding these factors is important for shaping policies and interventions aimed at improving population health. With the advancement of the technology, prediction using machine learning is oneof the alternatives in discovering the predictive factors that impact life expectancy. Therefore, the objective of this study is to identify and analyse the socio-health factors influencing life expectancy across countries using machine learning techniques. The study found that age group, immunisation status, and the presence of diseases such as HIV/AIDS were significant predictors of life expectancy.  \nThese insights are important for policymakers’ public health strategies and resource allocation.  \nKeywords: Contributing factor, Health, Life expectancy, Random forest, Regression  \n1 Introduction  \nThe word “life expectancy” was defined as the length of time that a living thing, especially a human being, is likely to live based on the Cambridge Dictionary [1] . The study of the contributing factors towards life expectancy has been the subject of a variety of research in the last few decades. However, with the advancement of the technology, there are still many problems associated with these issues that need to be studied and analysed in order to find solutions [2, 3] .  \nOne of the primary interests of medical research and national public health profile indicators is the extension of life expectancy, where it exhibited patterns of continuous growth over time with high variability between countries over the years [2,3] . The changes in life expectancy can be the result of changes in many factors, including health, environmental, and economic development factors [4-6] . For adults in the United States (US), adopting a healthy lifestyle contributes to the reduction of premature mortality and life expectancy extension [7], other than occupation and wage affecting socio-economic variation in life expectancy [8]. Life expectancies were associated with changes in income [9], advanced education, experienced better health outcomes and health satisfaction [10], healthcare expenditures, healthcare resources, mortality rates, the prevalence of Human Immunodeficiency Virus (HIV), and health outcomes [11] .  \nBesides, annual pharmaceutical expenditures, decreasing tobacco consumption, or increasing consumption of vegetables and fruits can also increase life expectancy [11] . There was a report stating that people are healthy and live longer where the average life expectancy was estimated to increase by 7 years until 2025 from 1997, with life expectancy of 80 years by 26 countries. However, the variations in life expectancy still existed between countries of high and low-income groups, which may include ASEAN and other developing countries. Furthermore, investigating related contributing factors helps governments to suggest alternatives in increasing life expect","cbCaikf2312QRo8k","https://ap.wps.com/l/cbCaikf2312QRo8k","pdf",703373,1,12,"English","en",105,"# Abstract\n# Introduction\n## Definition and importance of life expectancy\n## Socio-economic and health determinants\n## Prior research and motivation\n## Study objective and scope","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To identify and analyze socio-health factors influencing life expectancy across countries using machine learning techniques.\"},{\"question\":\"Which factors were found to be significant predictors of life expectancy?\",\"answer\":\"Age group, immunisation status, and diseases such as HIV/AIDS were identified as significant predictors.\"},{\"question\":\"Why is understanding socio-health contributing factors important for policymakers?\",\"answer\":\"It supports the development of public health strategies and helps guide resource allocation to improve population health.\"}]","The Impact of Socio-Health Factors Towards Life Expectancy across Countries using Machine Learning | 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