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Volume 3 | Issue 1 | Year 2024 | Article Id: DST-V3I1P101 DOI: https://doi.org/10.59232/DST-V3I1P101
Comparative Data Analysis on Fetal Health Using Machine Learning
Arosocohi Yosua Daeli, Dewi Agushinta R, Emirul Bahar, Sri Hayuningsih
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 07 Jan 2024 | 10 Feb 2024 | 23 Feb 2024 | 08 Apr 2024 |
Citation
Arosocohi Yosua Daeli, Dewi Agushinta R, Emirul Bahar, Sri Hayuningsih. “Comparative Data Analysis on Fetal Health Using Machine Learning.” DS Journal of Digital Science and Technology, vol. 3, no. 1, pp. 1-10, 2024.
Abstract
This research aims to find a machine learning classification algorithm to classify fetal health decisions using existing features of fetal health. The classification algorithms compared are support vector machine, logistic regression, and Random Forest Classifier. After hyperparameter tuning, the results obtained from this research are that the first support vector machine model obtained an accuracy of 0.88, a precision of 0.80, a recall of 0.76, and an f-1 score of 0.78. The second model is logistic regression, which gets an accuracy of 0.87, precision of 0.80, recall of 0.69, and f-1 score of 0.73. The third model is a Random Forest Classifier, which gets an accuracy of 0.94, precision of 0.91, recall of 0.88, and f-1 score of 0.89. Of these three algorithms, random forest is the best algorithm for detecting fetal health.
Keywords
Classification, Fetal health, Logistic Regression, Machine Learning, Random Forest Classifier, Support Vector Classifier.
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