Abstract: Machine learning is gradually becoming more prevalent in the realm of medical diagnostics. Designing a Disease Prediction Model with the help of rapid development Application in the field of medical science with using machine Learning, would be helpful for the Doctor to predict and diagnose the diseases at the initial stage to take the necessary precautions. This Prediction is possible with the use of Machine learning techniques, libraries, Statistics and the EMR tool. The Calculation of the Prediction is carried out by implementing various Supervised Machine Learning Algorithms like Decision Tree, Random Forest and KNN. A large set of data can be processed in this system for improvising the accuracy level of Prediction. The data are split. An EMR tool records the data of the patients. The trained dataset were made to compare with test cases. To avoid multicollinearity, a feature has to be extracted and a Statistical Analysis must be carried out. We produce an accuracy level of 90% through this model. It can only predict whether the patient is effected or not and a precautionary steps to be taken. The Predicted results are sent to the patient’s Mail ID. The Data are plotted by making us of confusion Matrix with a Binary data Labels i.e., Quantity Analysis.

Keywords: EMR, Disease Prediction, DT, RF, KNN.

| DOI: 10.17148/IARJSET.2022.9666