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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Dugguh Sylvester Aondonenge
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.
Artificial Intelligence -Robotic Process Automation on Enterprise Architecture in the Telecommunications Industry Isa Abdulrazaq Imam; Ajayi Ore-Ofe Ore-Ofe; Abubakar Umar; Dako Daniel Emmanuel; Dugguh Sylvester Aondonenge; Lawal Abdulwahab Olugbenga
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 3 (2024)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i3.36736

Abstract

This paper explores the strategic impact of Artificial Intelligence (AI)-enhanced Robotic Process Automation (RPA) on Enterprise Architecture (EA) within the telecommunications industry. Traditionally, RPA has been applied to automate repetitive tasks without altering underlying IT infrastructure, focusing primarily on operational efficiency. However, the integration of AI introduces cognitive capabilities to RPA, enabling more dynamic interactions within complex organizational systems. This paper assesses how AI-driven RPA can influence EA by enhancing system efficiency, supporting business-IT alignment, and promoting digital transformation. Through case studies and analyses of various telecommunications operations, the paper investigates the dual role of AI-enhanced RPA in both streamlining enterprise-wide processes and maintaining adaptability to meet industry demands. The findings indicate that, while AI-RPA integration holds significant promise for accelerating operational improvements, it also presents unique challenges related to governance, scalability, and long-term sustainability. This work contributes insights into the adoption of AI-driven RPA as a transformative tool for telecommunications, offering guidance on best practices for aligning automated systems with enterprise strategic goals.Additionally, the study provides a structured framework for integrating AI-driven RPA into EA using ArchiMate and TOGAF modeling methodologies, emphasizing its potential to drive scalability, improve governance, and ensure alignment with strategic business objectives
Co-Authors Abubakar Umar Ajayi Ore-Ofe Ajayi Ore-Ofe Ore-Ofe Dako Daniel Emmanuel Ibrahim Ibrahim Isa Abdulrazaq Imam Kamorudeen Hassan Taiwo Lawal Abdulwahab Olugbenga