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All Journal Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal Eksplora Informatika Respati Jurnal Abdi Insani Community Development Journal: Jurnal Pengabdian Masyarakat Information System Journal (INFOS) Journal of Intelligent Software Systems Indo-Fintech Intellectuals: Journal of Economics and Business Journal of Future Artificial Intelligence and Technologies
Akhmad Dahlan
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Journal : Journal of Future Artificial Intelligence and Technologies

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Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification Safriandono, Achmad Nuruddin; Setiadi, De Rosal Ignatius Moses; Dahlan, Akhmad; Rahmanti, Farah Zakiyah; Wibisono, Iwan Setiawan; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-12

Abstract

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.
Co-Authors Achmad Nuruddin Safriandono Ahmad Muktamar B Amalia, Mekar Meilisa Ambarwati, Dwi Arini Ayu Amri Amri Amri Andriyani, Widyastuti Anggono, Eko Tri Arief Zikry Budi Witjaksana, Budi De Rosal Ignatius Moses Setiadi Ema Utami Emha Taufiq Luthfi Farah Zakiyah Rahmanti Iwan Setiawan Wibisono Lendra Lendra LILIS DWI FARIDA Lukman Lukman M. NURAMINUDIN M. Nuraminudin Muhammad Bitrayoga Nina Dwi Putriani Nina Dwi Putriani Nofirman, Nofirman Ojugo, Arnold Adimabua P.D.P., Bambang Purba, Ulfani Ikhwana Renaldi Renaldi Reni Reni Reni, Reni Rospita, Andri Siska Yulia Defitri Siska Yulia Defitri Susilo Suharsono Yoga Pristyanto Yuliana, Muhammad Rafi Nur