Heart Diseases Diagnosis Using Neural Networks Arbitration

Abstract

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

“…Olaniyi and Oyedotun [18] proposed a three-phase model based on the ANN to diagnose heart disease in angina and achieved a classification accuracy of 88.89%. Moreover, the proposed system could be easily deployed in healthcare information systems.…”

Diagnosis and Identification of Risk Factors for Heart Disease Patients Using Generalized Additive Model and Data Mining Techniques

“…5-9-11 In health care, data mining or statistical machine learning plays a vital role in the medical applications including diagnosis, prognosis, and therapy. 12 Clinical data mining involves the conceptualization, extraction, analysis, and interpretation of the available clinical data for practical knowledge-building, clinical decision making, and partition reflection. 12 A medical diagnosis is a classification problem 13 In the predictive data mining, the data set consists of instances, each instance is characterized by attributes or features and another special attribute represents the outcome variable or the class.…”

Classification of medical datasets using back propagation neural network powered by genetic-based features elector

“…Table 1 covers papers published in the recent years for each dataset used in this work. [10] 2015 Confusion matrix 81.89 [11] 2017 Logistic regression 80.43% Iraqi Diabetes [12] 2015 Improved RIPPER 100% [5] 2016 Genetic algorithm and K-means 98% Lung cancer [13] 2015 Back Propagation 96% [14] 2017 Logistic Regression 77.4% [15] 2017 RBF Neural Network 81.25% Breast cancer (WBCD) [13] 2015 ANN based on migration method 99.97% [16] 2016 Genetic algorithm and K-means 98% [5] 2017 ANN and genetic algorithm for training 100% Statlog (heart) [17] 2015 SVM 87.5% [16] 2016 Genetic algorithm and K-means 87% [18] 2016 Fuzzy with gradient descent 85.8 [19] 2017 Fuzzy Petri Nets 75%…”