Optimizing Disease Prediction using Machine Learning Techniques and Personalized Nutrition and Diet Plan

The growth in the chronic disease burden of cardiovascular disorders, respiratory syndromes, and asthma is predominantly attributed to unwholesome dietary habitss. To make amends from this problem, there has to be an adequate nutrition and health plan. In this paper, a health and nutrition recommendation framework with the ability to offer individualized diet timetables, nutritional advice, and disease prediction is suggested. With the analysis of user information including foods eaten, weight, age, and medical conditions like diabetes and thyroid, the system provides customized diet plans to encourage more healthy eating. The diet planning and nutrition advice modules employ organized logic to parse user information and provide balanced meals and nutritional guidelines. These characteristics help provide nutrition counseling to the users based on their health condition so that nutritional needs can be met accordingly and illnesses can be avoided. In disease prediction, the system uses machine learning to understand symptoms, gender, and age and identify possible health risks and predict probable diseases. Through database searches and backtracking user inputs, the model provides preventive information for probable health issues. Very high accuracy rate of 88% of Random Forest classifier employed in disease prediction with rigorous preprocessing, training, and testing in precision and confusion matrix measure parameters. The system takes into account physical characteristics, i.e., intake of diet, weight, and age, and health conditions, i.e., chronic disease, for the prescription of most suitable diet regimes and individualized nutrient suggestions. Eventually, this system will promote overall health and wellness with data-driven insight to enhance nutrition and prevent chronic disease.