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All Journal JOIV : International Journal on Informatics Visualization
Zakaria, Noor Hidayah
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Systematic Literature Review: An Early Detection for Schizophrenia Classification Using Machine Learning Algorithms Azizi, Ainin Sofiya; Kamal, Marnisha Mustafa; Azizan, Nurzarifah; Zawawi, Rohaizaazira Mohd; Zakaria, Noor Hidayah; Salamat, Mohamad Aizi; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2446

Abstract

Schizophrenia is a complex mental health disorder that poses significant challenges in diagnosis and treatment due to its multifaceted symptoms, such as hallucinations, delusions, and cognitive impairments. Early detection is crucial for effective intervention, yet traditional diagnostic methods often fail in precision and scalability. This systematic literature review investigates the application of machine learning (ML) algorithms in the early detection and classification of schizophrenia. By synthesizing findings from 40 primary studies, the review highlights the effectiveness of diverse ML models, including Random Forests, Support Vector Machines (SVM), and advanced deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Key datasets such as clinical records, EEG signals, and neuroimaging data were analyzed to evaluate model performance across metrics like accuracy, precision, and sensitivity. Studies demonstrated that hybrid approaches, integrating multiple data sources and deep learning architectures, achieved classification accuracies exceeding 90%, with notable advancements in early-stage diagnosis. However, the review identifies critical challenges, including data quality issues, biases, and limited external validation, which hinder the widespread clinical application of these models. Through a comparative analysis of ML methods and traditional supervised approaches, the study underscores the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized treatment plans. Addressing current limitations, such as expanding data diversity and improving model interpretability, is essential for translating these findings into practical healthcare solutions. This research contributes to the growing knowledge in ML-driven diagnostics, advocating for its integration into clinical workflows to optimize schizophrenia management.
Exploring Current Methods and Trends in Text Summarization: A Systematic Mapping Study Ahmad Raddi, Muhammad Faris Faisal; Hassan, Rohayanti; Zakaria, Noor Hidayah; Sahid, Mohd Zanes; Omar, Nurul Aswa; Firosha, Ardian
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.1654

Abstract

This paper presents a systematic mapping study of the current methods and trends in text summarization, a challenging task in natural language processing that aims to condense information from one or multiple documents into a concise and coherent summary. The paper focuses on applying text summarization for the Malay language, which has received less attention than other languages. The paper employs a three-phased quality assessment procedure to filter and analyze 27 peer-reviewed publications from seven prominent digital libraries, covering 2016 to 2024. The paper addresses two research questions: (1) What is the extent of research on text summarization, especially for the Malay language and the education domain? and (2) What are the current methods and approaches employed in text summarization, with a focus on addressing specific problems and language contexts? The paper synthesizes and discusses the findings from the literature review and provides insights and recommendations for future research directions in text summarization. The paper contributes to advancing knowledge and understanding of the state-of-the-art techniques and challenges in text summarization, particularly for the Malay language.
Co-Authors Ahmad Raddi, Muhammad Faris Faisal Azizan, Nurzarifah Azizi, Ainin Sofiya Firosha, Ardian Kamal, Marnisha Mustafa Nurul Aswa Omar, Nurul Aswa Rohayanti Hassan, Rohayanti Sahid, Mohd Zanes Salamat, Mohamad Aizi Yulherniwati, - Zawawi, Rohaizaazira Mohd