An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

• Published in: IEEE access Vol. 7; pp. 180235 - 180243
• Main Authors: Javeed, Ashir, Zhou, Shijie, Yongjian, Liao, Qasim, Iqbal, Noor, Adeeb, Nour, Redhwan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Developing countries; Diagnostic systems; Diseases; Failure detection; Feature extraction; feature selection; grid search algorithm; Heart; Heart diseases; Heart failure; hyperparameters optimization; LDCs; Machine learning; Model accuracy; Prediction algorithms; random search algorithm; Search algorithms; Solid modeling; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2952107

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.