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All Journal JOIV : International Journal on Informatics Visualization
Masrah Azrifah Azmi Murad, Masrah Azrifah
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Computer Science & IT

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A Model for Enhancing Pattern Recognition in Clinical Narrative Datasets through Text-Based Feature Selection and SHAP Technique Dalhatu, Sirajo Muhammad; Azmi Murad, Masrah Azrifah
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.8.4.3664

Abstract

Clinical narratives contain crucial patient information for predicting cardiac failure. Accurate and timely cardiac failure recognition (CFR) significantly impacts patient outcomes but faces challenges like limited dataset sizes, feature space sparsity, and underutilization of vital sign data. This study addresses these issues by developing a methodology to improve CFR accuracy and interpretability within clinical narratives. Four datasets—the Framingham Heart Study, Heart Disease from Kaggle, Cleveland Heart Disease, and Heart Failure Clinical Records—undergo preprocessing, including handling missing values, removing duplicates, scaling, encoding categorical variables, and transforming unstructured data using natural language processing (NLP). Various feature selection methods (Chi-Squared, Forward Selection, L1 Regularization) are used to identify influential features for CFR, and the SHapley Additive exPlanations (SHAP) technique is integrated to improve interpretability. Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models are trained and evaluated. Performance was evaluated using accuracy, precision, recall, f1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results indicate that L1 Regularization with LR and Chi-Squared with RF perform best for specific datasets. The final model, combining all datasets with Forward Selection and RF, achieves high accuracy (91%), precision (87%), recall (97%), f1-score (91%), and AUC-ROC (94%). This study concludes that advanced text-based feature selection and SHAP interpretability significantly enhance CFR model accuracy and transparency, aiding clinical decision-making. Future research should incorporate more diverse datasets, explore advanced NLP techniques, and validate models in various clinical settings to enhance robustness and applicability.
Information Behavior Model of e-Health Literacy for Online Health Information-seeking Effectiveness Xuewen, Wang; Azmi Murad, Masrah Azrifah; ZhangLi, Wu; Ismail, Ismi Arif; Mohamed Shaffril, Hayrol Azril
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.8.4.3642

Abstract

This study examines the growing imbalance between the availability and demand for medical resources, rising healthcare costs, and the critical role of accessible health information in disease prevention and public health. The rapid advancement of information technology has established the Internet as a primary source of health information, leading to an overload that surpasses users' processing capacity and causes significant cognitive and emotional challenges. This phenomenon profoundly affects users' health information behavior and decision-making, particularly in self-health management. To address these challenges, eHealth literacy must incorporate an understanding of users' information behavior. This research analyzed the literature on eHealth literacy through a systematic review, identifying key components and categorizing them using Squiers' method. The findings reveal that current definitions fail to address the variability in online health information quality and lack a comprehensive model for understanding information behavior in an overloaded environment. As a solution, this study proposes a new definition of eHealth literacy: the capacity to efficiently search for, access, evaluate, and apply relevant information based on physiological, emotional, and cognitive needs when using electronic health resources. This new definition emphasizes discernment, proactive engagement, personalized use, and practical application of information in health management. The Information Behavior Model of eHealth Literacy (IBeHL) highlights eHealth literacy's multifaceted and dynamic nature, influenced by environmental factors, and recognizes both active information seeking and passive information exposure. Future research should focus on refining this model and exploring its potential to enhance health information behavior and decision-making.
Chatbot Adoption Model in Determining Student Career Path Development: Pilot Study Ahmed, Mohamed Hassan; Abdullah, Rusli; Jusoh, Yusmadi Yah; Azmi Murad, Masrah Azrifah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3798

Abstract

A career decision is incredibly essential in one's life. It shapes one's future role in society, influences professional development, and can lead to success and fulfillment. Making a sound and consistent career decision based on skills and interests is critical for personal and professional development. Since generative AI is an emerging and revolutionizing technology industry in the market, which is very good in generating contents, providing consultancies and answering questions in humanly fashion, integrating AI chatbots into the career planning process can help students to get more accurate and personalized advice for their future career. This pilot study emphasized the student’s adoption of chatbot technology for career selecting processes utilizing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model with four additional constructs which influence the student’s career selection, namely: Perceived Student’s External Factors (PEF), Perceived Student’s Interest (PSN), Perceived Career Opportunities (PCO) and Perceived Self-Efficacy (PSF). An online survey was conducted, and 37 responses were received and analyzed. The measurement model produced a promising result, and the discriminant validity, construct reliability and validity of the model were confirmed with a Cronbach’s alpha (α) above 0.70 threshold and AVE over 0.5 cut-off for most of the constructs including the four above mentioned latent variables. However, the Price Value (PPV) and Facilitating Conditions (PFC) UTAUT2 constructs produced alpha () of 0.680 and 0.611 respectively which is still adequate since their AVE is above the 0.5 threshold. Consequently, their interpretation and conclusions should be approached with caution.
Co-Authors Abdullah, Rusli Ahmed, Mohamed Hassan Dalhatu, Sirajo Muhammad Ismi Arif Ismail Jusoh, Yusmadi Yah Mohamed Shaffril, Hayrol Azril WANG Xuewen ZhangLi, Wu