Machine Learning Approach To Predict Multiple Diseases Based On Symptoms

Healthcare is being altered by machine learning for early sickness prediction and diagnosis. Based on patient symptoms, Random Forest, an ensemble machine learning algorithm, will predict diabetes, heart attack, cancer, TB, arthritis, lung disease, stroke, asthma, brain tumor, osteoporosis, COPD, and oral disease. A comprehensive dataset of patient records, symptoms, and sickness outcomes is curated to start the research. We manage missing data, encode category variables, and normalize numerical features. For accurate sickness prediction, feature selection methods identify the most essential symptoms and clinical traits. On the preprocessed data, a Random Forest classifier is trained to handle complex feature interactions without overfitting. Model performance is improved via hyperparameter tuning, and many measures are employed to ensure forecast accuracy. In addition to sickness prediction, the research analyzes feature importance to identify key symptoms and covariates. This may assist doctors understand disease start and progression. Health care providers utilize the final model to predict illnesses in real time using patient symptoms. We will regularly evaluate and update the model to incorporate new medical knowledge and data. Patient data privacy, ethics, and compliance with healthcare data laws and standards are paramount. The effort seeks unbiased, transparent, and prejudice-free disease prediction to ensure equitable healthcare results.