Abstract
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.
Highlights
Abnormal functionality of the heart due to any cause is known as heart disease
RANDOM FOREST MODEL DEVELOPED FOR HEART HEART FAILURE PREDICTION In this experiment, we develop only random forest model which is implemented in Python programming package
In this study, we highlighted the problem of overfitting in the recently proposed methods for heart failure prediction and proposed a novel learning system to facilitate the heart failure prediction
Summary
Abnormal functionality of the heart due to any cause is known as heart disease. There are different kinds of heart disease. The most common types are heart failure (HF) and Coronary Artery Disease (CAD). The major cause of heart failure (HF) is due to the blockage or narrowing down of coronary arteries. Coronary arteries supply blood to the heart. CAD is a prevalent kind of heart disease and well-known source of heart attacks in the world [1], [2]. HF is an expeditious healthcare problem [3] of the modern world and it has been reported that 26 million adults around the globe are suffering from HF [4]. HF disease was the leading cause of death globally in 2005, responsible for 17.5 million deaths, more than 80% of which occurred
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