Enhancing Disease Prediction Accuracy Using Random Forest

Abstract

MultiDisease prediction system” uses advanced machine learning techniques to facilitate identification Multiple diseases based on user -provided symptoms. The The system integrates classification algorithms, including random Forest, Support Vector Machine (SVM), K-Ner Closer Neighbors (KNN), support vector classifier (SVC) and logistics regression, to diagnose health conditions such as diabetes, gastroesophageal reflux disease (GERD), dengue, pneumonia and more than 20 other diseases. The proposed methodology is followed by a structured pipe involving data collection, function extraction, Pre -workment, model training, disease predictions, performance Evaluation and optimal selection of the model. It uses extensive Savets of medical data, extract relevant clinical traits, applies data Cleaning and normalization techniques and train machine learning models to increase diagnostic accuracy. During training The system predicts the likelihood of a disease -based disease and user input and evaluates the power of the model using metrics of key rating as accuracy, accuracy, appeal and f1-score to determine The most effective predictive model. This approach makes it easier for Disease detection, increases diagnostic reliability, supports personalized medical strategies and provides data -based data Help healthcare workers in clinical decision -making. According to Integration of machine learning into medical diagnostics, system It contributes to effective and accurate identification of diseases, permits Early medical intervention and finally improved patient results Key Words: Machine learning, disease prediction, medical diagnostics, classification algorithms, timely detection, health care analysis, clinical decision support, Random Forest, Logistic Regression, KNN, SVC.