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May 2023 Issue Vol.13 No.5
A NOVEL MORTALITY PREDICTION APPROACH IN CONGESTIVE HEART FAILURE PATIENTS USING RANDOM FOREST (RF) WITH INTENSITY WEIGHTED FIREFLY OPTIMIZATION (IWFFO)
https://ia600506.us.archive.org/21/items/ijitce-may-2023/IJITCE_May2023.pdf
Dr. C. Sowmiya
Department of Computer Science,
Government Arts and Sciences College, Aravakurichi, Karur (DT),Tamil Nadu, India
Dr. A. Divya
Department of Computer Science,
Government Arts and Sciences College, Aravakurichi, Karur (DT),Tamil Nadu, India
Abstract:
Heart related disease is the significant cause for short life of humans. People in large population country depend on healthcare industry, that’s why people need
accurate test result in short time. In healthcare industry, very huge amount of information is formed in daily large. A Random Forest algorithm with IWFFO proposed for the heart
disease prediction. The overall performance of the proposed method was compared with the prior Support Vector Machine (SVM) with Recursive Feature Elimination. Total number of
14 attributes from Cleveland heart disease dataset are selected. They are age, sex, cp, trestbps, chol, fbs, restecg,thalach, exang, oldpeak, slope, ca, thal, target. IWFFO
selectscp, trestbps, chol, fbs, thalach, exang, old peak, ca, thal, target. IWFFO feature selection method applies to RF algorithm which achieved high accuracy 98.7% when
compare to SVM algorithm.
Keywords: Data mining, Heart Disease,Classification, Feature Selection, Dataset.
