A Machine Learning Recommendation System for Disease Detection and Prevention Techniques Based on Using Age, Stress, and Medical Reports.

This paper presents a machine learning-based recommendation system for disease detection and prevention using age, stress levels, and medical reports. The system analyses patient data and extracts relevant features to predict disease probability using a Logistic Regression model. Based on the prediction, it provides personalized recommendations, including preventive measures and therapy options. A user-friendly interface allows users to input age, select stress factors, and upload medical reports for analysis. Experimental results show that combining age and stress factors improves prediction accuracy. The proposed system supports early diagnosis, reduces health risks, and enhances decision-making in preventive healthcare. In recent years, machine learning has emerged as a powerful tool in healthcare for analysing medical data, identifying patterns, and supporting clinical decision-making. This research focuses on the application of machine learning techniques for the prediction of human diseases, particularly by considering key factors such as age and stress. In this paper, machine learning algorithms such as Logistic Regression and Random Forest are employed for effective data suggest’ s and analysis from medical reports. These algorithms process patient information, including factors like age and stress, to identify patterns and predict potential diseases. Based on the extracted insights and prediction results, the system generates suitable treatment recommendations categorized into three major medical approaches: Allopathy, Homeopathy, and Ayurveda. It also provides multiple therapeutic options, enabling a more flexible and informed decision-making process in healthcare.