Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques

Abstract

The Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.

Comparative Study of Data Mining Techniques on Heart Disease Prediction System: a case study for the

“…Firstly, SVM performed feature selection to get better accuracy and then Features selection were implemented to decrease the noise or irrelevance data. From the experimental results, they Chaitrali S. Dangare et al, [5] used the Multilayer Perception Neural Network(MLPNN) with Backpropagation (BP) Algorithm and Weka tool in the developed Heart Disease System (HDS) and the performance results of these techniques were compared based on the accuracy. In the existing system, 13 types of Medical terms were used for the prediction process, which 2 new terms as Obesity and Smoking were included.…”

“…For example, classification can be used to prophesy (predict) whether the patient has heart Disease or the patient has no heart disease [5].…”

Score Level Fusion Based Death Prediction using Data Mining Techniques

“…S.Dhamodharan used Bayesian classification technique, which is one of the major classification models. The primary goal is to predict the class type from classes such as "Liver Cancer", "Cirrhosis", "Hepatitis" and "No Disease" [12].…”

Methodologies of Data Mining in Healthcare: A Inspection

“…Dealing with the issues and challenges of data mining in healthcare [10,11]. In order to predict the various diseases effective analysis of data mining is used [12][13][14][15][16][17][18][19][20][21]. Proposed a data mining methodology in order to improve the result [22][23][24] and proposed new data mining methodology [25,26] and proposed framework in order to improved the healthcare system [27][28][29][30][31].…”