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All Journal Indonesian Journal Of Educational Research and Review Advance Sustainable Science, Engineering and Technology (ASSET)
Abdillah
Universitas Muhammadiyah Mataram
Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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The Role of Mathematics in Machine Learning for Disease Prediction: An In-Depth Review in the Healthcare Domain Abdillah; Syaharuddin Syaharuddin; Vera Mandailina; Saba Mehmood
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.845

Abstract

The rapid advancements in healthcare technologies and the increasing complexity of medical data have made it imperative to explore and optimize predictive models for disease management. This study aims to conduct a systematic literature review to identify advancements, challenges, and opportunities in disease prediction using machine learning (ML) within the healthcare domain. The literature sources include Scopus, DOAJ, and Google Scholar, covering the period from 2013 to 2024. The findings reveal that both machine learning (ML) and deep learning (DL) algorithms have significant potential for disease prediction and treatment outcomes in various clinical contexts. Algorithms such as Random Forest, Logistic Regression, and ensemble techniques like Boosting have demonstrated strong performance in numerous studies. However, the effectiveness of these algorithms is highly context-dependent, including the type of disease, patient characteristics, and available data. Deep learning, particularly Convolutional Neural Networks (CNNs) and hybrid Long Short-Term Memory (LSTM) models, excels in handling complex, high-dimensional data, providing higher prediction accuracy compared to traditional ML models. This research shows that deep learning models, especially CNN and hybrid LSTM, achieve higher accuracy in disease prediction compared to traditional ML models. However, challenges related to data quality, privacy, and the underlying mathematical modeling of these algorithms remain to be overcome for wider applications.
The Role of Mathematics in Machine Learning for Disease Prediction: An In-Depth Review in the Healthcare Domain Abdillah; Syaharuddin Syaharuddin; Vera Mandailina; Saba Mehmood
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.845

Abstract

The rapid advancements in healthcare technologies and the increasing complexity of medical data have made it imperative to explore and optimize predictive models for disease management. This study aims to conduct a systematic literature review to identify advancements, challenges, and opportunities in disease prediction using machine learning (ML) within the healthcare domain. The literature sources include Scopus, DOAJ, and Google Scholar, covering the period from 2013 to 2024. The findings reveal that both machine learning (ML) and deep learning (DL) algorithms have significant potential for disease prediction and treatment outcomes in various clinical contexts. Algorithms such as Random Forest, Logistic Regression, and ensemble techniques like Boosting have demonstrated strong performance in numerous studies. However, the effectiveness of these algorithms is highly context-dependent, including the type of disease, patient characteristics, and available data. Deep learning, particularly Convolutional Neural Networks (CNNs) and hybrid Long Short-Term Memory (LSTM) models, excels in handling complex, high-dimensional data, providing higher prediction accuracy compared to traditional ML models. This research shows that deep learning models, especially CNN and hybrid LSTM, achieve higher accuracy in disease prediction compared to traditional ML models. However, challenges related to data quality, privacy, and the underlying mathematical modeling of these algorithms remain to be overcome for wider applications.
Exploration of Factors Influencing Student Engagement and Motivation in Animation-Based Math Learning Nurwahidah Nurwahidah; Syaharuddin; Abdillah; Vera Mandailina
Indonesian Journal of Educational Research and Review Vol. 8 No. 2 (2025): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/ijerr.v8i2.90315

Abstract

Low student engagement, motivation, and learning outcomes in mathematics learning are challenges for educators in creating an effective learning environment. This study aims to determine the level of student engagement, motivation, and learning outcomes in animation-based mathematics learning. This study uses meta-analysis, which examines a number of research results on similar problems that are taken purposively based on their suitability to the research theme. The data used are 97 articles in the last 10 years. Data collected from the Google Scholar, DOAJ, and Scopus indexing databases. The eligibility criteria are the requirements for research results to include the number of students (N), t-test values, F-test values, r-test values, and classical values. Then analyzed using meta-analysis through effect size and standard error values to see the summary effect size in the random effect model. The results of this study show that animation has become the center of attention of educators who want to use it as an effective learning medium. They are interested in exploring the potential of animation in increasing student motivation and engagement in the learning process by utilizing sophisticated technology and design, animation can bring learning to a more interesting level, stimulate imagination, and provide a deeper learning experience. However, students often face challenges in maintaining their motivation to learn, especially in complex learning environments. Therefore, teachers and educational developers can utilize animation as a strategy to improve the quality of mathematics learning in the classroom.
Co-Authors Nurwahidah Nurwahidah Saba Mehmood Syaharuddin Syaharuddin Syaharuddin Vera Mandailina, Vera