Risk prediction of cardiovascular disease using machine learning classifiers

Abstract

Cardiovascular disease (CVD) makes our heart and blood vessels dysfunctional and often leads to death or physical paralysis. Therefore, early and automatic detection of CVD can save many human lives. Multiple investigations have been carried out to achieve this objective, but there is still room for improvement in performance and reliability. This study is yet another step in this direction. In this study, two reliable machine learning techniques, multi-layer perceptron (MLP), and K-nearest neighbour (K-NN) have been employed for CVD detection using publicly available University of California Irvine repository data. The performances of the models are optimally increased by removing outliers and attributes having null values. Experimental-based results demonstrate that a higher accuracy in detection of 82.47% and an area-under-the-curve value of 86.41% are obtained using the MLP model, unlike the K-NN model. Therefore, the proposed MLP model was recommended for automatic CVD detection. The proposed methodology can also be employed in detecting other diseases. In addition, the performance of the proposed model can be assessed via other standard data sets.

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

“…This, we believe is not exhaustive, other evaluation assessments for its applicability in context will be examined in future research studies. Breast cancer risk prediction Support vector machine accuracy 97.13 [29] Breast cancer diagnosis Support vector machine, Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbors accuracy 97.2 [31] Treatment trend prediction for hypothyrodism Extra trees accuracy 84 [32] Chronic kidney disease prediction Random forest, support vector machine, decision tree accuracy 99.8, 95.5, 98.6 [33] Chronic disease progression (sclerosis) K-nearest neighbor, support vector machine, decision tree, logistic regression auc [34] Chronic kidney disease detection K-nearest neighbor, random forest, neural networks accuracy 0.993 [35] Advanced chronic kidney disease prediction Logistic regression, random forest, decision tree accuracy 81.9, 82.1, 82.1 [36] Prediction of hypertension Deep neural network, decision tree accuracy 0.75, 0.69 [37] Risk prediction (hypertension) k-nearest neighbor, multi-layer perceptron accuracy 82.47 [38] Prediction of hypertension Artificial neural network accuracy 82 [39] Heart disease risk prediction [40] Hypertension prediction extreme Gradient Boosting, Gradient Boosting Machine, Logistic Regression, Random forest, Decision tree and Linear Discriminant Analysis accuracy 83-90% [41] Spam detection Naive Bayes, decision tree, neural networks, random forest, support vector machine accuracy 96.9-99.66 [42] Spam detection ensemble accuracy 99.91 [43] Malicious spam in mails Naive Bayes, support vector machine, logistic regression and random forest accuracy 96.15, 96.15, 98.08, 95.38 [44] Sms spam classification Naive Bayes, BayesNet, C4.5, J48, Self-organizing map and Decision tree accuracy 89.64, 91.11, 80.24, 79.2, 88.24, 75.76 [45] Junk email detection Support vector, random forest accuracy 93.52, 91.41 [46] Email spam detection Bagging, random forest, decision tree (J48) accuracy 98 [47] Credit…”

“…The application of deep learning in the prediction and classification of hypertension with blood pressured related variables for which those positive for hypertension was 1883 and those without hypertension were 6266 [38], a comparative performance evaluation between deep neural network and decision tree classifier with four different datasets showed the following prediction accuracies; Deep neural network: (0.75, 0.739, 0.743, 0.743) and for Decision tree: (0.676, 0.684, 0.690, 0.680). Further risk prediction studies aid at improving prediction performance with reliable techniques [39] for cardiovascular diseases using ML techniques such as K-nearest neighbor and Multi-layer perceptron (MLP) showed a prediction accuracy of 82.47% for MLP. Similarly, a related study [40] on the prediction of hypertension using features such as patient demographics, past and current patient health condition and medical records for the determination of risk factors through the use of artificial neural network showed prediction accuracy of 82%.…”

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

“…Among them, the CatBoost classifier gives better results with 76% of accuracy. Pal et al [27] developed a CVD risk prediction system using two ML approaches such as multi-layer perceptron (MLP) and k nearest neighbor (KNN) using a public dataset. The experimental result secured 82.47% of accuracy and an AUC of 86.41%.Abdalrada et al [40] developed cardiac autonomic neuropathy prediction using ML approaches from diabetes patients.…”

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

“…As a result, despite the difficulty, early detection and prediction of CVD hypersensitivity in seemingly healthy patients is essential for determining the prognosis [ 13 ]. It remains difficult for cardiologists to diagnose and treat patients in their early stages [ 14 ]. Working with patient databases for patients with heart disease is a practical application.…”