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All Journal Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development) Journal Sensi: Strategic of Education in Information System Edu Komputika Journal
Prasetyo, Septian Eko
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Journal of Information Technology and Computer Engineering

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A Cardiotocographic Classification using Feature Selection: A comparative Study Prasetyo, Septian Eko; Prastyo, Pulung Hendro; Arti, Shindy
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 01 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.25-32.2021

Abstract

Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.
Co-Authors Ardhiansyah, Alfian Arti, Shindy Atmaja, Chandra Khaira, Mulil Muhammad Ardian, Muhammad Prastyo, Pulung Hendro Sudarni, Ajeng Rahma