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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Abdijalil Abdullahi
Faculty of Computing, SIMAD University

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A machine learning approach to cardiovascular disease prediction with advanced feature selection Abdikadir Hussein Elmi; Abdijalil Abdullahi; Mohamed Ali Barre
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1030-1041

Abstract

Cardiovascular diseases (CVDs) pose a significant global public health challenge, necessitating precise risk assessment for proactive treatment and optimal utilization of healthcare resources. This study employs machine learning algorithms and sophisticated feature selection techniques to enhance the accuracy and comprehensibility of CVD prediction models. While traditional risk assessment tools are valuable, they frequently fail to consider the myriad intricate factors that contribute to the heightened risk of CVD. Our methodology employs machine learning algorithms to analyze diverse healthcare data sources and produce advanced predictive models. The salient feature of this research lies in the meticulous application of advanced feature selection techniques, enabling the identification of pivotal factors within heterogeneous datasets. Optimizing feature selection enhances the interpretability of the model, reduces dimensionality, and improves predictive accuracy. The area under the ROC curve (AUC-ROC) score of the wrapper method model significantly decreased from 95.1% to 75.1% after tuning, based on empirical tests that supported the suggested method. This showcases its capacity as a tool for assessing premature CVD susceptibility and developing tailored healthcare strategies. The study highlights the significance of integrating machine learning with feature selection due to the widespread influence of cardiovascular diseases. Integrating this system has the potential to enhance patient care and optimize the utilization of healthcare resources.
Cybersecurity awareness among university students in Mogadishu: a comparative study Adnan Abdukadir Ahmed; Abdikadir Hussein Elmi; Abdijalil Abdullahi; Abdullahi Yahye Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1580-1588

Abstract

This study aimed to assess the level of cyber security awareness among graduate and undergraduate students in five universities in Mogadishu. The study used a one-way analysis of variance (ANOVA) to examine the difference in cyber security awareness levels between graduate and undergraduate students across five reputable universities. The questionnaire method was used to collect data from 250 graduate and undergraduate students from SIMAD, SIU, UNISO, Jamhuriya, and Mogadishu universities. The cross-tabulation result showed that there was a significant difference in cyber security awareness levels between the universities. Specifically, the results showed that students from SIMAD and Jamhuriya universities suffered from virus attacks, while SIU students struggled with password strength and social network misuse. Mogadishu students faced phishing and virus attacks, and UNISO students dealt with both virus attacks and password strength issues. The study recommended that universities educate their students and parents on safe internet usage and cybersecurity and monitor and secure their internet and computer services. Additionally, the authors recommended the development of cybersecurity software to help students use their data confidently and securely.
Co-Authors Abdikadir Hussein Elmi Abdikadir Hussein Elmi Abdullahi Yahye Ahmed Adnan Abdukadir Ahmed Mohamed Ali Barre