Cuckoo search based deterministic scale (CSDS) for computer aided heart disease detection

Abstract

Predicative analysis in medical domain for the computer-aided disease. Prediction becomes a crucial practice in regular clinical practices, which is since, the false alarming or delay in disease detection is inversely proportionate to the clinical experience of the medical practitioner. Unlike the other domains, the sensitivity that is the accuracy in disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more crucial in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimising machine learning approaches to achieve significance in computer-aided methods to perform predictive analysis on heart disease detection. Regarding this context, this manuscript is defining a supervised learning approach by cuckoo search based deterministic scale (CSDS) to perform heart disease prediction. The experimental study which indicates the significance of the proposed model is related to detection accuracy and sensitivity along with other performance metrics.