IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework

摘要: Accuracy plays a vital role in the medical field as it concerns with life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement which classifier produces best results. A specific may be better than others for dataset, but another could perform some other dataset. Ensemble classifiers proved to effective way improve accuracy. In this we present ensemble framework multi-layer enhanced bagging optimized weighting. The proposed model called "HM-BagMoov" overcomes limitations conventional performance bottlenecks by utilizing seven heterogeneous classifiers. evaluated five different heart datasets, four breast cancer two diabetes liver datasets one hepatitis dataset obtained from public repositories. analysis results show that achieved highest accuracy, sensitivity F-Measure when compared individual all diseases. addition this, also accuracy state art An application named "IntelliHealth" developed based used hospitals/doctors diagnostic advice.