A comprehensive study of machine learning for predicting cardiovascular disease using Weka and Statistical Package for Social Sciences tools

Artificial intelligence (AI) is simulating human processes by machines and software simulators to help humans in making accurate, informed, fast decisions based on data analysis. The medical field can make use of such AI because records are enormous with many overlapping parameters. Using in-depth classification techniques analysis be the first step identifying reducing risk factors. In this research, we evaluating a dataset cardiovascular abnormalities affecting group potential patients. We aim employ as Weka understand effect each parameter suffering from disease (CVD). utilizing seven classes, baseline accuracy, naïve Bayes, <em>k</em>-nearest neighbor, decision tree, support vector machine, linear regression, artificial neural network multilayer perceptron. classifiers assisted correlation-based filter select most influential attributes that may have an impact obtaining higher accuracy. Analysis results sensitivity, specificity, precision Statistical Package for Social Sciences (SPSS) illustrated. A tree method (J48) demonstrated its ability classify CVD cases high accuracy 95.76%.