Prediction and analysis of Rheumatic heart disease using kNN classification with ACO

In this work, the effectiveness of the popular classification techniques k-Nearest Neighbour (kNN) algorithm is integrated with Ant Colony Optimization (ACO) to predict the likelihood of getting heart disease. The analysis has been performed in two phases. In the first phase, the kNN classification is used to classify the test data. In the second phase, the ACO is used to initialize the population and search for the optimized solution. The dataset used in this work is Streptococcus Pyogenes bacteria that cause Rheumatic Fever, also known as Acute Rheumatic Fever (ARF). In this paper, a new algorithm kNNACO, an integrated approach is proposed and the performance is analysed based on accuracy and error rate.