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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 171 Documents
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The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems Luluang, Muhammad Zaqly; Irawati; Darwis, Herdianti
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.398

Abstract

This study analyzes the effect of domain filtering on drug recommendation systems based on association rule mining using the FP-Growth algorithm with Neural Collaborative Filtering (NCF) as a comparison. The dataset used was derived from patient medical records containing attributes such as complaints, diagnoses, and drug therapies, with a total of 1,000 patient transactions. To avoid data leakage, the dataset was randomly divided into 70% training data and 30% test data before the modeling process was carried out. Domain filtering was applied by limiting the rule structure so that complaints and diagnoses acted as antecedents and drugs as consequents. The performance of the recommendation system was evaluated using the Precision@5, Recall@5, and Normalized Discounted Cumulative Gain (NDCG@5) metrics. The results of the experiment show that the FP-Growth approach with domain filtering produces higher Precision@5 and NDCG@5 values than the non-filtering approach. The Wilcoxon Signed-Rank test shows that the difference is statistically significant, while effect size analysis using Cliff's Delta shows a practically meaningful impact. Furthermore, a comparison with Neural Collaborative Filtering shows that the collaborative filtering-based approach is less effective on transactional clinical prescription data with limited historical interactions. These findings indicate that integrating medical domain knowledge into FP-Growth can improve the clinical relevance and quality of drug recommendation rankings

Page 18 of 18 | Total Record : 171

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