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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Ajayi Ore-Ofe
Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
Computer Science & IT Engineering Mechanical Engineering Transportation

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Early Heart Disease Prediction Using Data Mining Techniques Dugguh Sylvester Aondonenge; Ajayi Ore-Ofe; Kamorudeen Hassan Taiwo; Abubakar Umar; Isa Abdulrazaq Imam; Dako Daniel Emmanuel; Ibrahim Ibrahim
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36735

Abstract

Heart disease is a leading cause of mortality worldwide, characterized by the buildup of plaque in the arteries, which can lead to severe cardiovascular complications. Predicting heart disease is complex due to the need to analyze multiple risk factors, such as age, cholesterol, and blood pressure. This study develops a predictive model for earlyheart disease detection using data mining techniques to enhance timely and accurate diagnosis. The model combines multiple machine learning timely and accurate diagnosis. The model combines multiple machine learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach to improve prediction accuracy and reliability. The methodology follows five phases: data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The hybrid model was trained and tested using an 80:20 data split and evaluated against various classification algorithms. It achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, outperforming individual models. These results highlight the effectiveness of the hybrid approach in supporting early interventionfor heart disease, particularly in healthcare settings with limited diagnostic resources. This study demonstrates that advanced data mining techniques provide a viable solution for improving patient outcomes through the early detection of heart disease.
Design of an Enterprise Network Terminal Security Solution Muhammad Idris Abubakar; Ajayi Ore-Ofe; Abubakar Umar; Ibrahim Ibrahim; Lawal Abdulwahab Olugbenga; Ajikanle Abdulbasit Abiola
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39105

Abstract

This paper presents a secure enterprise network terminal security solution designed to protect the confidentiality, integrity, and availability of critical data and network resources. It presents a logical approach to creating an enterprise network security architecture with a primary focus on optimizing and enhancing the performance of as data center servers and storage. Traditionally, network infrastructure has primarily focused security measures on core components, such as firewalls and intrusion detection/prevention systems (IDS/IPS). However, the exponential growth of Internet of Things (IoT) devices, Bring Your Device (BYOD) policies, and remote workforce trends has shifted the threat landscape, making network terminals key vectors for malicious access, with critical end devices often being the ultimate targets. This study presents a comprehensive framework that prioritizes terminal-level security by integrating existing encryption techniques, specifically a double layer VPN tunnel architecture, to enhance data transmission confidentiality. A significant contribution of the paper lies in its structured classification of network terminals into thoughtful, intelligent, and dumb categories based on capability and memory—an approach that supports tailored securityimplementations. The framework also outlines contingency measures for securing data center endpoints in the event of a breach scenario. The novelty of this work lies in its focused protection strategy for terminals within enterprise environments, bridging the security gap between endpoints and core infrastructure. The proposed solution demonstrates the potential to reduce exposure to ransomware and targeted attacks through layered defenses and a proactive disaster recovery and business continuity (DR&B) strategy, despite limitations in real-world simulation due to resource constraints.
Co-Authors Abubakar Umar Ajikanle Abdulbasit Abiola Dako Daniel Emmanuel Dugguh Sylvester Aondonenge Ibrahim Ibrahim Ibrahim Ibrahim Isa Abdulrazaq Imam Kamorudeen Hassan Taiwo Lawal Abdulwahab Olugbenga Muhammad Idris Abubakar