Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

Abstract

Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naïve Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics' diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

“…Tan et al [10] proposed a hybrid method in which two machine learning algorithms, Support Parthiban et al [12] diagnosed heart disease in diabetic patients using automatic learning methods. Naïve Bayes and SVM algorithms are applied using WEKA.…”

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

“…Many studies were carried out about heart disease diagnosis in all over the world generally using by artificial intelligence techniques or data mining methods. [5][6][7][8] The use of data mining techniques in medical diagnosis has been increasing gradually. There is no doubt that evaluations of data taken from patients and decisions of experts are the most important factors in diagnosis.…”

“…Many authors had used various classification techniques to this dataset for heart disease diagnosis. [5][6][7][8][9][10][11] but probably, SVM and MPLE are not been used under proper modeling scheme. This study shows high classification accuracy rate and presented a significant variable input importance chart for heart disease diagnosis.…”

Heart disease and data mining: collaboration in the making

“…The main purpose of this method is to locate the best hyper plane by disjoining the data from two classes as much as feasible with a suitable nonlinear mapping. Support Vector Machine requires the solution to perform classification of the optimization problem [26].…”