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All Journal Jurnal Elemen Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Advance Sustainable Science, Engineering and Technology (ASSET)
Saba Mehmood
Thal University Bhakkar
Aerospace Engineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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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.
Accuracy Comparison of Multivariate Newton-Raphson and Newton-Kantorovich Methods through Numerical Simulation in Nonlinear Systems Syaharuddin; Hendi Hidayah; Mahsup Mahsup; Saba Mehmood; Wasim Raza
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.1971

Abstract

Nonlinear systems of equations often appear in various fields of science and generally cannot be solved analytically, so numerical methods are required. However, previous studies have not provided a direct comparison of the accuracy and efficiency of the Multivariate Newton-Raphson method and the Newton-Kantorovich method when applied to the same nonlinear system, creating a gap in understanding their relative performance. This study aims to analyze and compare the performance of two numerical methods, namely the Newton-Raphson method and the Newton-Kantorovich method, in solving nonlinear systems of equations numerically. The evaluation is based on the convergence rate, result accuracy, and iteration efficiency of each method. The nonlinear system used involves trigonometric, exponential, and polynomial functions. Simulations were conducted twice using three equations directly for each method. The error tolerance was set at 0.001, with a maximum of 100 iterations. The simulation results showed that the Multivariate Newton-Raphson method had the best performance, requiring only 7 iterations to achieve convergence with a very small error of 2.711×10^(-7). In contrast, the Newton-Kantorovich method required 21 iterations and produced an error of 6.770×10^(-5), indicating slower convergence and lower efficiency. Based on these results, it can be concluded that the Multivariate Newton-Raphson method is the more accurate and efficient method for solving nonlinear systems of equations through numerical simulation. This finding contributes to the selection of an appropriate numerical method and opens opportunities for further exploration in higher-dimensional systems.
The geometric concepts of the Istana Dalam Loka traditional house: An ethnomathematics study Maulidia Dwi Meyundasari; Intan Dwi Hastuti; Syaharuddin Syaharuddin; Saba Mehmood
Jurnal Elemen Vol 10 No 2 (2024): May
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jel.v10i2.25208

Abstract

This study explores the philosophical aspects of traditional architecture and ethnomathematics geometry in the Istana Dalam Loka traditional house, located in West Nusa Tenggara. Employing a qualitative approach with an ethnographic design, data were gathered through observations and interviews with key, primary, and additional informants. Findings indicate that mathematical concepts are reflected in every architectural element, such as rectangles in doors, terraces, stairs, shields (kantar), kandaga, the sultan's palanquin (tandu), pineapple decoration, and frames. The study also identifies square concepts in the sultan's child's palanquin, glass boxes, and windows. Circular concepts are found in badong, and the concept of an isosceles triangle is present in the roof, offering potential for creative geometry learning. This research has implications in enriching students' mathematical education by utilising geometric examples from local culture. Integrating ethnomathematics into elementary school curricula can broaden students' cultural perspectives, increase student engagement in learning, and enrich students' understanding of mathematics.
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.
Co-Authors Abdillah Hendi Hidayah Intan Dwi Hastuti Mahsup Mahsup Maulidia Dwi Meyundasari Syaharuddin Syaharuddin Syaharuddin Vera Mandailina, Vera Wasim Raza