Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Abstract

Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

“…In [ 36 ], the author claims that the DT model consistently beats the NB and SVM models. Its results show that SVM achieves 87% accuracy and DT achieves 90% accuracy, as shown in [ 37 ], while LR achieves the maximum accuracy in the prediction of heart disease at what time when equated to DT, SVM, NB, and KNN. The prediction accuracy provided by the RF-based framework is 97% [ 38 ], with a specificity of 88% and a sensitivity of 85% for the evaluation of congenital heart disease.…”

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

“…The DT model, according to the author in [31], consistently outperforms the NB and SVM models. According to its findings, SVM achieves an accuracy of 87%, DT achieves an accuracy of 90%, and LR achieves the highest accuracy in heart disease prediction when compared to DT, NB, SVM, and KNN, as shown in [32]. For the assessment of congenital heart disease, the RF-based framework's prediction accuracy is 97 percent [33], with a specificity of 88 percent and a sensitivity of 85 percent.…”

An Ensemble Feature Optimization for an Effective Heart Disease Prediction Model

“…The accuracy is computed as follows Accuracy = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁 (18) where 𝑇𝑃 defines true-positive, 𝑇𝑁 defines truenegative, 𝐹𝑃 defines false-positive, and 𝐹𝑁 defines false-negative. The sensitivity is computed as follows Sensitivity = 𝑇𝑃 𝑇𝑃+𝐹𝑁 (19) The specificity is computed as follows Specificity = 𝑇𝑁 𝑇𝑁+𝐹𝑃 (20) The precision is computed as follow Precision = 𝑇𝑃 𝑇𝑃+𝐹𝑃 (21) The F-measure is computed as follows…”

“…In [17] is focused with design of the cardiac disease diagnosis decision support system. This research makes advantage of a stream of data from the OpenML repository that contains 1,000,000 records of cardiac illness and 14 characteristics [18][19][20]. Following the application of pre-processing and feature selection techniques, machine learning methods such as RF, DT, gradient boosted trees (GBT), SVM, LR, MLP utilised to evaluate the multi-classification and binary classification on the data stream [21][22][23].…”