The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods.
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Vankara, J., Lavanya Devi, G. PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound. Int J Speech Technol (2020). https://doi.org/10.1007/s10772-020-09670-6
- DICE similarity coefficient
- Differential evolution approach