Embedded Files
Heart Disease Prediction System (HDPS v2.0) - Trial Version
COPYRIGHT © 2017 BY UNIVERSITY OF MALAYA. ALL RIGHTS RESERVED
This system is a desktop based application developed using Java programming language and NetBeans IDE. Weka API is used to generate a prediction model proposed in our research from ARFF data file and predict the presence of a heart disease for a new patient with this model. For the training purpose, UCI Cleveland Heart Disease dataset was used to train the data.
Heart Disease Prediction Model
In this research, a classification model was proposed using the 9 significant features (sex, cp, fbs, restecg, exang, oldpeak, slope, ca and thal) and and the Vote technique.
Significant Features and Description:
sex - Gender of the patient (1 for male and 0 for female)
cp - Chest pain type described with 4 values:
Value 1: typical angina
Value 2: atypical angina
Value 3: non-anginal pain
Value 4: asymptomatic
fbs - Fasting blood sugar > 120 mg/dl; 1 if true and 0 if false
restecg - Resting electrocardiographic results in 3 values:
Value 0: normal
Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
exang - Exercise induced angina (1 for yes and 0 for no)
oldpeak - ST depression induced by exercise relative to rest
slope - The slope of the peak exercise ST segment
Value 1: upsloping
Value 2: flat
Value 3: downsloping
ca - Number of major vessels (0-3) coloured by fluoroscopy
Thal - The heart status described with 3 value:
Value 3: normal
Value 6: fixed defect
Value 7: reversable defect
Inventors
Mr. Mohammed Shafenoor Amin
Developer & Data Analyst
Dr. Chiam Yin Kia
Developer & Data Analyst
Dr. Kasturi Dewi
Developer & Data Analyst
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