Identification of Disease based on Symptoms by Employing ML

A precise and appropriate examination of any health-related condition is critical for disease detection and care. In the event of a critical illness, the standard method of detection may not be adequate. Creating a clinical diagnosis solution based on Machine learning (ML) methods for the prediction and diagnosis of any disease can aid in a more definitive diagnosis than the traditional way. M recognizes complex behaviour in massive quantities of data, which are subsequently developed to create medical forecasts in new datasets. In this research, a disease predicting system using ML algorithms is designed. The dataset was collected from Kaggle and pre-processed to make it compatible with the ML algorithms. Depending on individual personal symptoms, the diagnosing system predicts the condition that the person may be struggling from. When compared to the other algorithms, the XGBoostmethod produced the best outcomes. The classification performance of the model was 99.45%.Our diagnostics algorithm can function as a physician in the timely identification of an illness, ensuring that treatments can begin early so that lives can be saved.