A New Index for Intelligent Classification of Early Syndromic of Cardiovascular (CVD) Diseases Based on Electrocardiogram (ECG) (Published)
Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and ECG datasets. A quantitative research approach was used for the study with examination of several segments based on recommended framework. Three (3) classifier models were adopted to detect cardiac related problems using specified datasets. The classifiers such as; Random Forest Ensemble (RFE), Support Vector (SVM) Classifier and Artificial Neural Network (ANN) was employed for Machine Learning process. The models were implemented using a robust programming languages (Python and Jupyter notebook). The datasets were further segmented into two categories: training sets and testing in the ratio of 80:20 respectively. The test data reflects; precision, recall and sensitivity: Results show Radom Forest Model: 0.50 (50%) accuracy, 0.48 (48%) precision score and 0.65 (65%) recall sensitivity score (RSS), SVM classifier indicated 0.70 (70%) accuracy score, 0.47 (47) % precision score as well as 0.52 (52 %) sensitivity score. The ANN model illustrates 0.50 (50%) score for accuracy, precision and recall. Research Findings demonstrated that, RFE, SVM, ANN illustrate 100% accuracy in precision and recall sensitivity. The interaction effects of the various clinical factors influencing the CVD of patient was appraised and performance evaluation were further done using standard data science measures; Confusion Matrix (CM), MAP, MAPE, RMSE was deployed. The final results obtained shows that RFE, SVM, ANN models support satisfactorily the assessment and classification of early syndromic conditions of CVD.
Keywords: ANN, Accuracy, ECG, RFE, SVM Classifier, UUTH, precision score and recall sensitivity score (RSS).