Vol.:(0123456789) Distributed and Parallel Databases https://doi.org/10.1007/s10619-021-07329-y 1 3 A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset Bhanu Prakash Doppala 1  · Debnath Bhattacharyya 2  · Midhun Chakkravarthy 1  · Tai‑hoon Kim 3 Accepted: 3 March 2021 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Coronary illness can be treated as one of the major causes for mortality globally. On-time and Precise conclusion on the type of disease is signifcant for therapy and breakdown expectancy. Research scientists are working rigorously in their respective felds to reduce the death rate. Even though lot of research took place on this area still there is a scope for increasing the prediction accuracy. The fundamental point of our proposed work is to build up a hybrid methodology using genetic algorithm (GA) with (RBF) radial basis function (GA-RBF) for the detection of coronary sick- ness with increased accuracy using the feature selection mechanism. The proposed system performance achieved an accuracy of 85.40% using 14 attributes, and the prediction accuracy increased to 94.20% with nine characteristics where the func- tionality of the proposed system performed much better after attribute reduction. Keywords RBF network · Genetic algorithm · Attribute selection · Heart disease prediction · Classifcation * Tai-hoon Kim taihoonn@daum.net Bhanu Prakash Doppala bhanu.doppala@lincoln.edu.my Debnath Bhattacharyya debnathb@kluniversity.in Midhun Chakkravarthy midhun@lincoln.edu.my 1 Department of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia 2 Department of Computer Science and Engineering, K L Deemed to be University, KLEF, Guntur 522502, India 3 School of Economics and Management, Beijing Jiaotong University, Beijing, China