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All Journal International Journal of Integrative Sciences
Syah, Farhan Stiady
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Social Sciences

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Liver Disease Classification Using Decision Tree and Random Forest Algorithms Cahyono, Yoyo; Rosyani, Perani; Syah, Farhan Stiady; Putri, Firda Salsabila; Ashari, Idpan; Sofian, Kurnain
International Journal of Integrative Sciences Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v4i1.13509

Abstract

Diagnosing diseases using technology is no longer uncommon. With advancements in healthcare technology, decision-making, particularly in detecting liver diseases, has become more efficient. Liver, an essential human organ, sees its functionality decline in patients with liver diseases. According to WHO data (2013), 28 million individuals in Indonesia suffer from liver diseases, marking it as one of the ten deadliest diseases. Early detection is crucial for effective treatment. This study aims to predict liver diseases using the Random Forest algorithm. Feature selection and classifier choice are pivotal for improving accuracy and computational efficiency. Using the Liver Disease Patient Dataset, the study involved data analysis, preprocessing, algorithm modeling, and visualization. Results show the Random Forest algorithm achieved an accuracy of 0.713326 with an F1 score of 81%
Co-Authors Ashari, Idpan Cahyono, Yoyo Perani Rosyani Putri, Firda Salsabila Sofian, Kurnain