Predicting the Presence of Heart Diseases using Comparative Data Mining and Machine Learning Algorithms
Daniel Ananey-Obiri, Enoch Sarku
Abstract
Heart disease, an example of cardiovascular diseases is the number one notable reason for the death of many people in the world. Of recent, studies have concentrated on using alternative efficient techniques such as data mining and machine learning in the diagnosis of diseases based on certain features of an individual. This study will use data exploratory and mining techniques to extract hidden patterns using python. By this, machine learning algorithms (logistic linear regression, decision tree classifier, Gaussian Nave Bayes models) will be developed to predict the presence of heart diseases in patients. This will try to seek better performance in predicting heart diseases to reduce the number of tests require for the diagnosis of heart diseases. The k-fold cross validation approach will be used in assessing the resulting models for receiver operating characteristic (ROC) curves (sensitivity against specificity). The dataset was collected from UCI machine learning repository which contains information on patients with heart disease. The dataset has 14 attributes and measured on 303 individuals.