Predicting Multiple Diseases Using Machine Learning Algorithms

The Predicting Multiple Diseases system examines the user-provided symptoms as input and produces the disease identification as an output. All patient-related information is included in the data used in the healthcare sector. However, no universal architecture has been put out in the healthcare sector for predicting more than one disease. Thus, this project will provide whether the patient had disease. So, this work enables to predict the diseases and level of it by these algorithms SVM, KNN, XGBOOST. Diabetes is a group of illnesses that cause elevated blood sugar levels. The main Idea is determine whether a person has diabetes or not Using KNN Algorithm. So, firstly use the KNN algorithm to train the data and then predict the person having diabetes. And SVM algorithm will compare with the KNN to know which gives better output for predicting the Diabetes disease from the given Dataset with inputs like BMI, Glucose, Blood Pressure, Skin Thickness, Insulin and Age. For predicting heart disease XGBoost is used and stands as Extreme Gradient Boosting. It is also scalable. Since it can perform parallel processing on a single machine that is what makes it quick. And using XGBoost the prediction takes place by importing the library and will find the Accuracy.