An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

Abstract

Data mining techniques have been applied magnificently in many fields including business, science, the Web, cheminformatics, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the stateof-the-art research on heart disease diagnosis and prediction. Specifically in this paper, we present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbour classification (KNN), support vector machine (SVM), and artificial neural networks techniques. Results show that SVM and neural networks perform positively high to predict the presence of coronary heart diseases (CHD). Decision trees after features reduction is the best recommended classifier to diagnose cardiovascular disease (CVD). Still the performance of data mining techniques to detect coronary arteries diseases (CAD) is not encouraging (between 60%-75%) and further improvements should be pursued.Index Terms-heart disease, data mining, decision tree, naive bayes, K-nearest neighbor, support vector machine 310

Evaluating the Accuracy of Classification Algorithms for Detecting Heart Disease Risk

“…In this algorithm, univariate trees are created as output. The classification rules are constructed in the form of a decision tree [7,21].…”

Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring

“…For , this process many researchers are using unsupervised methods such Kmeans [22], c-fuzzy clustering [23] methods and some are following -if then else‖ rules to segregate the dataset . In the field of supervised learning models, linear classifiers [14] such as perceptron learning models , Linear discriminant model, logistic regression [24] , lasso regression [25] [26] and probability models such as Naïve Bayes [27] are widely used in medical classification tasks . Methods such as Neural networks [28] , Support vector machine [29], etc., are popular when the classification problems are typically multi-class and the nature of dataset is non-linear .…”

Optimum parametreler yardımıyla performansı artırılmış KNN algoritması tabanlı kalp hastalığı tahmini

“…Alzahani vd. [16] kalp hastalıkları veri kümesi üzerinde özellik seçimi yapmış ve özellik seçimi yöntemlerinin teşhis başarısına artırdığını raporlamışlardır. Dolatabadi vd.…”