Early Prediction of Cerebrovascular Disease using Boosting Machine Learning Algorithms to Assist Clinicians

Clinicians are required to make an early prediction of diseases save a life, especially cerebrovascular diseases. The objective this research is use mathematical models such as boosting machine learning algorithms tool be applied by clinicians for disease. This paper particularly, considered XGBoost, AdaBoost, LightGBM, and CatBoost Classifiers predict disease using age, gender, BMI, hypertension, heart disease, residence type, ever married, smoking status, average glucose level the patients. Synthetic Minority Over-Sampling Technique Edited Nearest Neighbors Under-sampling (SMOTE-ENN) Feature Engineering were dataset enhance performance algorithms. result obtained showed that XGBoost Classifier best model with accuracy 98% AUC 0.983.