Feature optimization by discrete weights for heart disease prediction using supervised learning
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METHODOLOGIES AND APPLICATION
Feature optimization by discrete weights for heart disease prediction using supervised learning Fuad Ali Mohammed Al-Yarimi1 • Nabil Mohammed Ali Munassar2 • Mohammed Hasan Mohammed Bamashmos2 • Mohammed Yousef Salem Ali2
Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The topic predictive analytics is the ray that lightning the way to patch the gap between accuracy in decision-making by the expertise and the inexperience. In particular, the health domain is more crucial about disease prediction accuracy. The disease diagnosis by clinical practitioner correlates to his exposer toward the clinical observations of the disease. However, the perceptions of an experienced clinical practitioner on a medical record often fail to identify the premature states of the disease, which costs patient life in the sector of critical diseases such as heart diseases. Hence, contemporary computer science engineering research has more attention to define substantial predictive analytics built by machine learning toward heart disease prediction. The critical objective to define predictive analytics with minimal false alarming is centric to potential training data corpus, and the optimal feature selection. In order to these arguments, the contribution of this manuscript aimed to portray the feature selection approach to perform supervised learning and label the given patient record is prone to heart disease or not with minimal false alarming. The contribution is a dynamic n-gram Features Optimization by Discrete Weights of the feature correlation. The experimental study signified the performance of the proposed model compared to the contemporary methods of feature selection for heart disease prediction. Keywords Coronary Heart Disease (CHD) Decision Trees (DT) Nearest Neighbor Algorithm (K-NN) Support vector machines (SVM) Decision support system (DSS)
1 Introduction The causes of cardiovascular disease have been many, with the prominent ones being diabetes, hypertension, and high LDL cholesterol with high triglycerides, blood sugar
Communicated by V. Loia. & Fuad Ali Mohammed Al-Yarimi [email protected]; [email protected] Nabil Mohammed Ali Munassar [email protected]; [email protected] Mohammed Hasan Mohammed Bamashmos [email protected]; [email protected] Mohammed Yousef Salem Ali [email protected]; [email protected] 1
Department of Computer Science, King Khalid University, Muhayil Aseer, Kingdom of Saudi Arabia
2
Department of Computer Science, University of Science and Technology, Taizz, Yemen
levels, obesity, and lack of required physical activity. The complexity of factors leading to the identification of cardiovascular diseases made researchers apply methods like data mining and neural networks. The prominent and primary medical methods used for identifying the severity of heart diseases are ECG, MRI, BP Checking, and physical tests. The various computer data methods used are K-Nearest Neighbor
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