Int J Med Rev 2020 Jan;7(1): 15-24

10.30491/ijmr.2020.221474.1078

Copyright © 2020 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://

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cited.

INTERNATIONAL JOURNAL OF

MEDICAL

REVIEWS

Narrative Review

Analysis of Weka Data Mining Techniques for Heart Disease

Prediction System

Basma Saleh *, Ahmed Saeidi, Ali al-Aqbi, Lamees Salman

Al-Mustansiriyah University, Baghdad, Iraq

* Corresponding Author: Basma Saleh Al-Mustansiriyah University, Baghdad, Iraq. Email: eng.basmaj@uomustansiriyah.edu.iq

Received December 01, 2019; Accepted December 27, 2019; Online Published January 01, 2020

Abstract

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for

data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining

techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect

a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this

reduced test plays an important role. Heart disease is a cardiovascular disease that causes death. Health problems are enormous in this recent

situation because of the prediction and the classification of health problems in different situations. The data mining area included the prediction

and identification of abnormality and its risk rate in these domains. Today the health industry holds hidden information essential for decision-

making. For predicting heart problems, data extraction algorithms like K-star, J48, SMO, Naïve Bayes, MLP, Random Forest, Bayes Net, and

REPTREE are used for this study (Weka 3.8.3) software. The results of the predictive accuracy, the ROC curve, and the AUC value are combined

using a standard set of data and a collected dataset. By applying different data mining algorithms, the patient data can be used for diagnosis as

training samples. The main drawbacks of the previous studies are that they need accurate and the number of features. This paper surveys recent

data mining techniques applied to predict heart diseases. And Identifying the major risk factors of Heart Disease categorizing the risk factors in an

order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hypertension, stress, etc. Data

mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life.

Keywords: Data mining, Heart Disease, Prediction, Classification, Dataset, WEKA tool, Prediction techniques.

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Analysis of Weka Data Mining Techniques for Heart Disease Prediction System

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Table 1. Commonly used Algorithms

Generic Name WEKA Name

Bayesian Network Naïve Bayes(NB)

Support Vector Machine SMO

C4.5 Decision Tree J48

K-Nearest Neighbor 1Bk

Workbench

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Figure 1. Weka GUI chooser.

Figure-2. The Explorer Interface

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Figure-3. The Knowledge Flow Interface

Figure-4. The Experimenter Interface

𝑀𝐶𝐶 =𝑇𝑃∗𝑇𝑁−𝐹𝑃∗𝐹𝑁

√(𝑇𝑃+𝐹𝑃)(𝑇𝑃+𝐹𝑁)(𝑇𝑁+𝐹𝑃)(𝑇𝑁+𝐹𝑁)

Analysis of Weka Data Mining Techniques for Heart Disease Prediction System

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Table-3. different outcomes of a two-class prediction

Actual class

Predicted class

YES No

YES True Positive (TP) False Negative (FN)

No False Positive (FP) True Negative (TN)

𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁

RMSE = √1

𝑛∑ (

𝜌𝑖,𝑗−𝜏𝑗

𝜏𝑗)2

𝑛𝑗=1

𝜌𝑖,𝑗

𝜏𝑗

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