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Journal : Building of Informatics, Technology and Science

Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Co-Authors ., Muslih Abdus Salam, Abdus Abdussalam Abdussalam Abu Salam Abu Salam Acun Kardianawati Ade Surya Ramadhan Adelia Syifa Anindita Aisyah, Ade Nurul Aisyatul Karima Aisyatul Karima Ajib Susanto Al zami, Farrikh Alzami, Farrikh Andi Danang Krismawan Ardytha Luthfiarta Ari Saputro Ari Saputro, Ari ARIANTO, EKO Ariya Pramana Putra Ariyanto, Noval Budi Harjo Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Firman Wahyudi, Firman Ghulam Maulana Rizqi Guruh Fajar Shidik Hafiidh Akbar Sya'bani Hanif Setia Nusantara Hanny Haryanto Hasan Aminda Syafrudin Hendy Kurniawan Herfiani, Kheisya Talitha Irfannandhy, Rony Irwan, Rhedy Isinkaye, Folasade Olubusola Izza Khaerani Ja'far, Luthfi Junta Zeniarja Karima, Nida Aulia Khafiizh Hastuti Khafiizh Hastuti Lucky Arif Rahman Hakim Maulana Ikhsan Megantara, Rama Aria Mira Nabila Mira Nabila Muhammad Jamhari Muslih Muslih Muslih Muslih Nurhindarto, Aris Ocky Saputra, Filmada Oki Setiono Pulung Nurtantio Andono Raihan Yusuf Rama Aria Megantara Ramadhan Rakhmat Sani Reza Pahlevi, Mohammad Rizky Rizqy, Aditya Rofiani, Rofiani Saputra, Filmada Ocky Saputri, Pungky Nabella Sarker, Md. Kamruzzaman Sendi Novianto Silla, Hercio Venceslau Soeleman, M Arief Sya'bani, Hafiidh Akbar Umi Rosyidah Valentino Aldo Wellia Shinta Sari Wildanil Ghozi