Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques. Issue 9 (18th June 2022)

• Record Type: Journal Article
• Authors: Theerthagiri, Prasannavenkatesan; Vidya, Jyothiprakash
• Other Names: Herrero Álvaro guestEditor.; Urda Daniel guestEditor.; Sedano Javier guestEditor.; Quintián Héctor guestEditor.; Corchado Emilio guestEditor.; Ahmed Syed Hassan guestEditor.; Khan Murad guestEditor.; Guibene Wael guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-18
• Subjects: cardiovascular disease -- classification of cardiovascular diseases -- feature ranking -- gradient boosting -- machine learning -- recursive feature elimination; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13064
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Cardiovascular diseases are one of the most common chronic illnesses that affect people's health. Early detection of cardiovascular diseases's can reduce mortality rates by preventing or reducing the severity of the disease. Machine learning algorithms are a promising method for identifying risk factors. This article proposes a recursive feature elimination‐based gradient boosting algorithm in order to obtain accurate heart disease prediction. The patients' health record with important cardiovascular disease features has been analysed for the evaluation of the results. Several other machine learning methods were also used to build the prediction model, and the results were compared with the proposed model. The results of this proposed model infer that the combined recursive feature elimination and gradient boosting algorithm achieves the highest accuracy (89.7%). Further, with an area under the curve of 0.84, the proposed algorithm was found superior and had obtained a substantial gain over other techniques. Thus, the proposed gradient boosting algorithm will serve as a prominent cardiovascular disease estimation and treatment model.