sarina maleki

Methods of detecting Coronary Artery Disease (CAD) are often prone to error and are also expensive and painful for the patient; therefore, the development and introduction of accurate machine learning-based methods for diagnosing this condition is of high importance. This research aimed to help detect coronary artery disease using the Harris Hawks Optimization (HHO) algorithm and machine learning techniques.

In this research, a novel approach based on feature selection was employed through a combination of HHO and machine learning techniques such as a Decision Tree (DT) and k-Nearest Neighbors algorithm (k-NN). To evaluate the proposed approach, we used two datasets (Cleveland & Z-Alizadeh-Sani) with medical records of 303 patients, and the evaluation was conducted by means of python 2016.

On the basis of the findings of this research, feature selection by using the Harris hawks optimization algorithm in combination with machine learning methods resulted in an increase in the accuracy of the results in such a way that in the case of Z-Alizadeh-Sani dataset, the percentage of accuracy in combination with a decision tree was equal to 0.98 and in combination with the k-nearest neighbors algorithm was equal to 0.78. Furthermore, the results of the Cleveland dataset showed that using the HHO in combination with a decision tree led to 88 percent accuracy and in combination with the k-nearest neighbors algorithm led to 77 percent accuracy. However, in the case of using all of the features (HHO only mode), accuracy was lower in all cases. Therefore, the HHO algorithm in combination with the decision tree was able to achieve the highest accuracy in diagnosing CAD in the feature selection mode compared to using all of the features.

The results from this study showed that the Harris hawk optimization algorithm in combination with machine learning techniques can have a positive role in the process of selecting effective features in diagnosing coronary artery disease.

With 17 million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included 303 cases. Each case had 14 features with the final medical status of cases (CAD or normal case) as one of the features where 165 and 138 cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.