EARLY PREDICTION OF LIFESTYLE DISEASES

The growing burden of lifestyle-related diseases necessitates innovative strategies for prevention and early intervention. This paper proposes a framework utilizing data analytics, machine learning, and potentially deep learning to empower individuals and healthcare providers in proactively managing health risks. By analyzing user-provided lifestyle data, the model aims to predict the likelihood of developing specific diseases associated with unhealthy habits, such as heart disease, diabetes, and certain cancers. This predictive approach can enable individuals to take timely preventive measures and healthcare professionals to tailor interventions for high-risk individuals. Ultimately, this framework strives to promote a healthier population by fostering proactive risk mitigation and personalized healthcare.