Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal The Indonesian Journal of Accounting Research The Indonesian Journal of Computer Science
Emmanuel Imuede Oyasor
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Using the Machine Learning Algorithms for Accurate Prediction of Diabetes Emmanuel Imuede Oyasor; Gbadebo, Adedeji Daniel
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4488

Abstract

Diabetics has proven to be the most threatening illness affecting the body system. It is associated with many consequences, including blindness, kidney failure, amputations, heart failure, microvascular and macrovascular complications, which affects millions of people across the world and has contributed to increased mortality. Studies shows that effective management and early detection of diabetes remains crucial for preventing its complications and improving the patient. According to available data, we use machine learning algorithms, including the Support Vector Machine (SVM), AdaBoost (ADA), Neural Networks (NNET), K-Nearest Neighbors (KNN), Random Forest (RF), and Logit Boost (LOGIT), for the accurate prediction of diabetes amongst patients. We find that the Logit Boost and AdaBoost stand out as the top performers for predicting diabetic patients, with balanced and reliable performance across various evaluation metrics. They exhibit high accuracy, strong AUC scores, and good overall performance across multiple metrics, making them suitable for this classification task. Neural Networks show excellent precision and low log loss, indicating strong probabilistic predictions, but their lower specificity suggests a higher false-positive rate. Random Forest demonstrates good recall but lower accuracy on the test set, indicating potential overfitting to the training data. SVM and KNN perform the weakest across most metrics, suggesting they may not be the best choices for this prediction task.
Asset Diversification and Pension Fund Performance in the West African Economy: Asymmetric Evidence from Nigeria Adedeji Daniel Gbadebo; Abiola Olaide Ayodele; Emmanuel Imuede Oyasor
The Indonesian Journal of Accounting Research Vol 29, No 2 (2026): IJAR May 2026 in Progress
Publisher : The Indonesian Journal of Accounting Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33312/ijar.868

Abstract

This study aims to investigate the asymmetric effects of pension fund asset allocation on the return on investment (ROI) of pension fund administrators in Nigeria using the Nonlinear Autoregressive Distributed Lag (NARDL) framework. Employing monthly data spanning 2007 to 2023, the analysis examines the roles of federal government securities, equities, corporate bonds, money market instruments, mortgage funds, and real estate assets in shaping pension fund performance. The findings reveal that while government securities remain the dominant investment vehicle, their long-term returns are constrained by inflationary pressures and interest rate volatility. Diversification into equities, corporate bonds, and real estate contributes positively to ROI, though the effects are modest and statistically weak in both the short and long run. Importantly, the symmetry tests indicate that increases and decreases in asset allocation do not yield significantly different outcomes, suggesting that diversification strategies remain effective regardless of market direction. Policy implications point to the need for regulatory reforms that gradually liberalize asset allocation guidelines, encourage market deepening, and promote innovative portfolio management practices. The study concludes that a balanced approach between safety and diversification is critical for enhancing pension fund performance, safeguarding retiree welfare, and supporting Nigeria’s broader developmental agenda.
Co-Authors Abiola Olaide Ayodele Adedeji Daniel Gbadebo Gbadebo, Adedeji Daniel