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All Journal ILKOM Jurnal Ilmiah International Journal of Informatics Engineering and Computing
Anjani, Sarah
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Computer Science & IT

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Journal : International Journal of Informatics Engineering and Computing

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Detecting Acute Liver Diseases Using CNN Algorithm Anjani, Sarah; Maria Yohana Jawa Betan
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.45

Abstract

This study tackles the critical challenge of detecting Acute Liver Failure (ALF) using machine learning algorithms. The main goal is to assess the effectiveness of several algorithms, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting, in accurately classifying cases of ALF. For this purpose, a comprehensive dataset with 8,785 records and 30 features from Kaggle is utilized, involving thorough preprocessing steps like feature selection, data cleaning, and normalization. The research emphasizes achieving high precision in ALF detection. Results show that CNN outperforms other algorithms, achieving a precision score of 1.00 for identifying ALF cases, demonstrating its high reliability. This study highlights the importance of algorithm selection in complex medical diagnoses, showcasing the potential of deep learning methods in healthcare and paving the way for more accurate and timely ALF detection to improve patient outcomes.
Co-Authors Abednego Dwi Septiadi Aina Musdholifah Br Bangun, Elsi Titasari Hamda, Hizbullah Maria Yohana Jawa Betan Sapuletea, Chrissandy Yudha Islami Sulistya