Disease Detection and Dietary Suggestions using Machine Learning

Infectious diseases have posed a global threat to human health, while diet plays a pivotal role in managing such health challenges. Contemporary lifestyles often include sedentary behaviors, poor dietary practices, and heightened stress levels, all of which can elevate the risk of various chronic illnesses. The complexity among social, environmental, economic, and behavioral factors contributes to the escalating prevalence of diseases in modern society. Balancing these factors necessitates comprehensive public health strategies, encompassing the promotion of healthy lifestyle, enhancement of healthcare accessibility and sanitation, mitigation of environmental pollution, and addressing social and economic disparities. By employing modern technology, particularly machine learning will enable disease prediction by considering vast datasets to detect complex patterns. Leveraging diverse data types like medical records, genetic information, lifestyle factors, and environmental data, Machine Learning (ML) algorithms can construct predictive models for analyzing a wide spectrum of diseases. This study mainly focuses on disease prediction based on different symptoms, supplemented by dietary recommendations and disease descriptions facilitated by a random forest classifier. Overall, this study integrates machine learning algorithms with healthcare information to provide personalized disease predictions, dietary advice, and disease insights in order to make informed decisions concerning their health.