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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INSPIRAMATIKA Unisda Journal of Mathematics and Computer Science (UJMC) Contemporary Mathematics and Applications (ConMathA) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Pengabdian Masyarakat: BAKTI KITA International Journal of Computing Science and Applied Mathematics-IJCSAM
Siti Amiroch, Siti
Universitas Islam Darul 'Ulum Lamongan
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Early Risk Prediction of Rheumatoid ArthritisUsing a Hybrid Restricted Boltzmann Machine Riska Rahma Yanti; Siti Amiroch; Awawin Mustana Rohmah; Dicka Yale Kardono
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9705

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmunedisease that requires early detection to prevent permanent jointdamage. The main challenge in predicting RA risk lies in thecomplexity of clinical data, which is multidimensional and oftenimbalanced. This study employs a Hybrid Restricted BoltzmannMachine (H-RBM) approach to improve the accuracy of earlyRA risk prediction. The RBM method is used as a featureextractor to map non-linear relationships within clinical data,which is then combined with three classification algorithms:Support Vector Machine (SVM), Random Forest (RF), andLogistic Regression (LR). The research data was sourced fromKaggle, comprising 4,425 samples that were balanced using theSynthetic Minority Oversampling Technique (SMOTE). Testingwas conducted with three data split ratios (70:30, 80:20, and90:10) using GridSearchCV for hyperparameter tuning. Theexperimental results show that the RBM-SVM model dominatesabsolutely with the highest performance at the 90:10 ratio,yielding an accuracy of 99.82%, an AUC score of 1.0000, and arecall of 100%. The perfect recall rate indicates that the modelis capable of identifying all positive patients without any missedcases (zero false negatives). Meanwhile, the RBM-RF and RBM-LR models achieved optimal performance at an 80:20 ratio withaccuracies of 93.95% and 83.42%, respectively. Overall, RBMintegration proved significant in extracting more informativelatent information compared to raw clinical features, therebymaking a crucial contribution to a precise RA early detectiondecision support system
Co-Authors Agus Mutia Aisyah Tur Rif’atin Nurdini Ali Shodikin Anekawati, Anik Arta MS, Carly Marshanda Avriono Aritonang Awawin Mustana Rohmah Awawin Mustana Rohmah Bernadus Anggo Seno AjiAji Dicka Yale Kardono Dzulfikar Fauzi, Mohammad Eva Wahyu Listyawati Evi Eka Andini Fifin Nur Aini Helisyah Nur Fadhilah Heny Ekawati Haryono Ida Dwi Lestari Kiratama, Danang Kuzairi Mohammad Hamim Zajuli Al Faroby Mohammad Syaiful Pradana, Mohammad Syaiful Mu’tasim Billah Novita Eka Chandra Nurdini, Aisyah Tur Rif’atin Nurmadhani, Nadya Rahmat Sigit Hidayat Riska Rahma Yanti Rohmaniah, Siti Siti Daiyatul Hamidah Tony Yulianto Umi Maftukhah