A Cardiotocographic Classification using Feature Selection: A comparative Study
| Autor: | Pulung Hendro Prastyo, Shindy Arti, Septian Eko Prasetyo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Cardiotocography
medicine.diagnostic_test lcsh:T Computer science business.industry Health condition Pattern recognition Feature selection Fetal health Classification lcsh:Technology lcsh:QA75.5-76.95 Random forest Machine Learning Data set Feature (computer vision) medicine Data Mining lcsh:Electronic computers. Computer science Artificial intelligence Feature Selection business |
| Zdroj: | JITCE (Journal of Information Technology and Computer Engineering), Vol 5, Iss 01 (2021) |
| ISSN: | 2599-1663 |
| DOI: | 10.25077/jitce.5.01.25-32.2021 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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