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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Science, Engineering, Technology, and Education
Osman, Nurul Aida
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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A Conceptual Framework for Personalized Early Prediction of Asthma Exacerbation Attacks Using Proximal Policy Optimization Aliyu, Dahiru Adamu; Patah Akhir, Emelia Akashah; Osman, Nurul Aida; Yahaya, Saidu; Adamu, Shamsudden; Mamman, Hussaini
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2944

Abstract

Asthma, a chronic respiratory ailment affecting millions worldwide, presents significant challenges due to the unpredictable nature of exacerbation episodes. Existing methodologies struggle to accurately predict exacerbations individually, particularly across diverse patient demographics. This paper introduces an innovative conceptual framework for the early prediction of asthma exacerbations, leveraging advanced reinforcement learning (RL) techniques, specifically proximal policy optimization, along with patient-specific data and environmental factors. The primary goal is to revolutionize asthma management by providing customized predictions and tailored reward mechanisms that enable proactive interventions and optimize resource allocation. The framework comprises critical components such as patient profiling through a mobile application, trigger identification, a RL-based predictive model, an early warning mechanism, and a personalized reward scheme. Data for patient profiling is gathered through a mobile application, which includes medical history, demographics, symptoms, and triggers. Profiling forms the foundation for the prediction model, enabling it to identify subtle patterns and anticipate exacerbation events more accurately and efficiently. The significant contributions of this research include offering a novel approach by incorporating custom reward functions to enhance learning across heterogeneous patient populations, tailoring interventions to unique triggers and symptom presentations, and addressing challenges associated with patient diversity. By addressing the limitations of existing methodologies and offering a comprehensive solution, this research promises significant improvements in asthma care and healthcare delivery, paving the way for future advancements in personalized medicine and predictive healthcare systems.
Robust Principal Component Analysis in Multivariate Applications Mutalib, Sharifah Sakinah Syed Abd.; Yusoff, Wan Nur Syahidah Wan; Kurniati, Angelina Prima; Osman, Nurul Aida; Zulhelmy, Zulhelmy
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3948

Abstract

Principal component analysis (PCA) is one of the multivariate methods that aims to construct components, each of which contains a maximal amount of variation from the data unexplained by the other components. Classical PCA (CPCA) is based on the classical covariance, which is easily affected by outliers. Additionally, the outliers may distort the PCA result. Robust statistics is one of the methods to overcome the outliers. Therefore, in this study, an alternative technique for robust principal component analysis (RPCA) is presented. Index Set Equality (ISE) is used as a robust estimator to robustify the CPCA and hence produce robust PCA (RPCA). The Hawkins-Bradu Kass dataset is used to illustrate the robustness of RPCA towards outliers compared to CPCA. The score plot, diagnostic plot, and performance measures are used to evaluate and illustrate the robustness of the RPCA. From the plots and performance measures, RPCA successfully identifies all outliers and is unaffected by masking and swamping effects. However, CPCA can only detect 0.2857 outliers, has 0.7143 masking effects, but does not have swamping effects. This study shows that RPCA, by using ISE as the robust estimator is a promising approach.
Co-Authors Adamu, Shamsudden Aliyu, Dahiru Adamu Angelina Prima Kurniati Mamman, Hussaini Mutalib, Sharifah Sakinah Syed Abd. Patah Akhir, Emelia Akashah Yahaya, Saidu Yusoff, Wan Nur Syahidah Wan Zulhelmy