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All Journal International Journal of Engineering, Science and Information Technology JOT
Ardi Kho
Pelita Harapan University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Machine Learning-Based Heart Failure Worsening Prediction Model to Build Self-Monitoring Prototype as an Effort to Prevent Readmissions and Maintain Quality of Life Untung Rahardja; Kristoko Dwi Hartomo; Indrajani Sutedja; Ardi Kho; Muhammad Farhan Kamil
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1467

Abstract

Heart failure is a long-term condition of great concern which calls for health care services in cycles. This significantly hampers quality of life for patients and increases costs for the healthcare systems. If the worsening of heart failure could be detected early, the intervention to prevent readmission could be employed, such that readmission would be avoided, enhancing the quality of life for the patient. Accordingly, the paper explains how such a model to predict the worsening of heart failure in patients who are at high risk of this condition has been developed. The model uses information gathered from the Electronic Health Records (EHRs) (Clinical Variables, Vitals, Test Results, and Demographics) to make accurate predictions on patients. As an effective and efficient approach towards achieving this goal, comparison of different algorithms such as random forests, support vector machines and gradient boosting has been employed towards the building of the final model. At this stage, the model is embedded into a user-friendly self-monitoring device, allowing the chronic heart failure patients to assess health indices on the fly with the help of the mobile app and wearable devices. This secondary prevention strategy makes patients more responsible for their health and decreases the number of patients readmitted to the hospital by increasing their functioning and well-being. The paper further projects the future development of other forms of treatment for chronic heart failure, especially at the first line, focusing primarily on the timing and succession.
Emotion-Aware Digital Health Platforms Supporting Psychological Well-Being through Positive Computing Approaches Risma Haris; Ardi Kho; Efa Ayu Nabila; Nova Syahrani Arasid; ⁠Abdullah Arif Kamal
Journal of Orange Technology Vol. 2 No. 2 (2026): April
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v2i2.62

Abstract

The rapid advancement of digital health technologies creates new opportunities to enhance psychological well-being beyond symptom reduction. Emotion-aware systems, grounded in positive computing approaches, enable digital platforms to respond adaptively to users’ affective states and promote sustained well-being rather than merely addressing negative symptoms. This study examines the development and quantitative evaluation of an emotion-aware digital health platform designed to support psychological well-being through positive computing principles. A quantitative research design was employed to assess the effectiveness of the platform. The system integrates self-reported emotional scales and behavioral interaction data to detect users’ emotional states in real time. Psychological well-being was measured using validated instruments, including the WHO-5 Well-Being Index, administered before and after platform usage. Statistical analysis was conducted to evaluate changes in well-being indicators and emotional awareness levels. The findings demonstrate a statistically significant improvement in users’ psychological well-being, positive affect, and emotional awareness after interacting with the platform. These results provide empirical evidence that integrating emotion-awareness into digital health systems can generate measurable humanistic impact and contribute to sustainable psychological well-being through positive computing approaches.
Co-Authors Efa Ayu Nabila Indrajani Sutedja Kristoko Dwi Hartomo Muhammad Farhan Kamil Nova Syahrani Arasid Risma Haris Untung Rahardja ⁠Abdullah Arif Kamal