Paper Title
Multi Disease Detection System Using Machine Learning

Abstract
Early detection of critical diseases remains a big difficulty in today’s healthcare. Many existing models either focus on detecting multiple common diseases or predict a single disease using algorithms like Support Vector Machines (SVM), Decision Trees, Naïve Bayes, and Logistic Regression. This limitations can lead to delayed diagnosis and treatment, affecting patient outcomes and survival rates. This research presents a multi-disease detection system using machine learning, incorporating advanced techniques of machine learning such as random forest and convolutional neural networks (CNNs) to predict six high-mortality diseases: heart, kidney, liver, breast cancer, pneumonia, and brain tumor diseases. According to WHO statistics, these are death causing diseases worldwide and required timely diagnosis for effective medical intervention. This system aims to reduce disease-related mortality, minimize treatment delays, and lower healthcare costs. Additionally, the platform offers personalized diet, food, exercise, and doctor recommendations, providing a comprehensive approach to health management. Overall, this Multi-Disease Detection System leverages advanced machine learning techniques to improve early diagnosis, enhance treatment outcomes, and empower users with personalized health recommendations, setting the foundation for more efficient and accessible healthcare solutions. Keywords - Multi-Disease Detection, Random Forest, Convolutional Neural Networks (CNN), Machine Learning, Recommendations, High-Mortality Diseases.