HMV: A medical decision support framework using multi-layer classifiers for disease prediction

• Extensive research has been conducted on disease prediction.
• However, there is no agreement on which classifier produces the best results.
• An optimal combination of classifiers is presented with multi-layer classification.
• The ensemble approach uses bagging with multi-objective optimized weighted.
• Comparison with existing techniques shows superiority of our ensemble.

Decision support is a crucial function for decision makers in many industries. Typically, Decision Support Systems (DSS) help decision-makers to gather and interpret information and build a foundation for decision-making. Medical Decision Support Systems (MDSS) play an increasingly important role in medical practice. By assisting doctors with making clinical decisions, DSS are expected to improve the quality of medical care. Conventional clinical decision support systems are based on individual classifiers or a simple combination of these classifiers which tend to show moderate performance. In this research, a multi-layer classifier ensemble framework is proposed based on the optimal combination of heterogeneous classifiers. The proposed model named “HMV” overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on two different heart disease datasets, two breast cancer datasets, two diabetes datasets, two liver disease datasets, one Parkinson's disease dataset and one hepatitis dataset obtained from public repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several well-known classifiers as well as ensemble techniques. The experimental evaluation shows that the proposed framework dealt with all types of attributes and achieved high diagnosis accuracy. A case study is also presented based on a real time medical dataset in order to show the high performance and effectiveness of the proposed model.