Predicting Lifestyle-Related Diseases Early: The NNSVM Methodology

Abstract

Chronic conditions including diabetes, heart disease, and hypertension are becoming major worldwide health issues, and a growing number of them are related to lifestyle decisions. Even though conventional medical statistics like blood pressure and glucose levels are significant markers, they only provide a portion of the picture. The key to early disease identification and prevention is frequently found in lifestyle choices, such as what we eat, how much exercise we get, and whether or not we smoke. In this work, we explore how machine learning might be used to anticipate these illnesses by including a person's everyday routines and behaviors in addition to reviewing medical records. In doing so, we developed a prediction model that is more accurate and comprehensive, revolutionizing the way we approach illness prevention in a society that is becoming more and more data-driven. The study develops a more thorough method of illness prediction by combining standard medical data with lifestyle habits. Algorithms like Support Vector Machine (SVM), Random Forest, Decision Trees, and Artificial Neural Networks (ANNs) are evaluated for their accuracy, precision, recall, and F1-score. According to the study's findings, lifestyle decisions have a significant impact on the development of chronic illnesses, and including them in disease prediction models increases the accuracy of those predictions overall. Combining many data sources is crucial for more accurate health outcomes, as the Random Forest model shows to be the most effective in forecasting diseases connected to lifestyle choices due to its capacity to manage intricate, non-linear interactions between variables. Early detection lowers the chance of serious health consequences by enabling prompt medical intervention, lifestyle modifications, and preventive measures. This strategy lowers healthcare expenses by reducing the need for costly follow-up therapies, while also improving patient outcomes.