Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 2
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Prediction Of Lifestyle Diseases Using Random Forest

Author(s) Dr. M. Sangeetha, S. Harshitha, K. A. Aberami, S. Abivarshini, S. Akalya
Country India
Abstract Health issues such as high blood pressure, diabetes, and heart disease are now some of the biggest challenges facing people worldwide. These problems not only shorten lives but also reduce overall quality of life. Since family members often share daily habits, it is important to look at health risks not just for individuals, but for households as a whole. In this study, we introduce a prediction system that uses the Random Forest algorithm to estimate the risk of lifestyle-related diseases for different family members. The system collects key health indicators—like blood pressure, heart rate, and body temperature—along with time stamps, and then uses them to provide personalized risk assessments. The results are grouped into three levels: Normal, Caution, and Critical. This helps detect potential health issues early and makes it easier to take quick action. In addition to individual reports, the system also gives a general picture of the health of the family giving informative insights into common trends and threats. We experimented to demonstrate that the accuracy, stability and generalization of Random Forest is superior to that of traditional single algorithms. The framework is also streamlined and flexible and can be used in a broad variety of healthcare settings and health care environments, particularly, where resources are limited. On the whole, this article proves that machine learning can be a useful tool in preventive medicine, as it helps track health indicators in the earlier stages of life, motivate to make more healthy choices, and minimize the long-term consequences of lifestyle diseases.
Keywords Chronic Illnesses, Random Forest, Health Monitoring, Risk Prediction, Preventive Care
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-02
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E-ISSN 3048-7641
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