Alabi Waheed BANJOKO

@unilorin.edu.ng

Lecturer, Faculty of Physical Sciences
Lecturer, Faculty of Physical Sciences
University of Ilorin

https://researchid.co/awb2023

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Modeling and Simulation, Multidisciplinary, Applied Mathematics

Scopus Publications

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

A multi-objective optimization algorithm for gene selection and classification in cancer study
Alabi W. Banjoko, Waheed B. Yahya, and Oyebayo R. Olaniran
Elsevier BV

Genetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology
Kazeem A. Dauda, Kabir O. Olorede, Alabi W. Banjoko, Waheed B. Yahya, and Yusuf O. Ayipo
CRC Press

Investigation on Determinants and Choice of Contraceptive Usage among Nigeria Women of Reproductive Age

Efficient data-mining algorithm for predicting heart disease based on an angiographic test
Alabi Waheed Banjoko, , Kawthar Opeyemi Abdulazeez, and
Penerbit Universiti Sains Malaysia
Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

Weighted support vector machine algorithm for efficient classification and prediction of binary response data
A W Banjoko, W B Yahya, M K Garba, and K O Abdulazeez
IOP Publishing
Abstract This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.

Multiclass Response Feature Selection and Cancer Tumour Classification With Support Vector Machine
A. W. Banjoko, W. B. Yahya, and M. K. Garba
Knowledge E
Background & Aim: In this study, efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multi-category tumour classes of biological samples using gene expression profiles was proposed. Methods: Feature selection interface of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate which ensured efficient detection of false-positive genes. The selected gene subsets using the above method were further screened for optimality using the Misclassification Error Rates yielded by each of them and their combinations in a sequential selection manner. In a 10-fold cross-validation, the optimal values of the SVM parameters with appropriate kernel were determined for tissue sample classification using one-versus-all approach. The entire data matrix was randomly partitioned into 95% training set to train the SVM classifier and 5% test set to evaluate the predictive performance of the classifier over 1,000 Monte-Carlo cross-validation runs. Published microarray breast cancer dataset with five clinical endpoints was employed to validate the results from the simulation studies. Results: Results from Monte-Carlo study showed excellent performance of the SVM classifier with higher prediction accuracy of the tissue samples based on the few gene biomarkers selected by the proposed feature selection method. Conclusion: SVM could be considered as a classification of multi-category tumour classes of biological

RECENT SCHOLAR PUBLICATIONS

A Multi-Objective Optimization Algorithm for Gene Selection and Classification in Cancer Study
AW Banjoko, WB Yahya, OR Olaniran
Applied Soft Computing, 112911 2025

High Resolution Class I HLA-A,-B, and-C Diversity in Eastern and Southern African Populations
AW Banjoko, T Ng’uni, N Naidoo, V Ramsuran, O Hyrien, ZM Ndhlovu
bioRxiv 2024

Genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology
KA Dauda, KO Olorede, AW Banjoko, WB Yahya, YO Ayipo
Computational Approaches in Biomaterials and Biomedical Engineering 2024

Investigation on Determinants and Choice of Contraceptive Usage among Nigeria Women of Reproductive Age
AW Banjoko, WB Yahya, MK Garba, RB Afolayan, KA Dauda, ...
Journal of Biostatistics and Epidemiology 9 (1), 35-50 2023

The nexus between foreign aid and foreign direct investment in Nigeria: simultaneous equations approach
MK Garba, SB Akanni, AW Banjoko, TC Oladele
Malaysian Journal of Computing (MJoC) 8 (2), 1620-1638 2023

Effects of foreign direct investment on economic growth: evidence from Nigeria and Rwanda
MK Garba, AO Sikiru, WB Alabi, RB Afolayan
Kabale University Interdisciplinary Research Journal 1 (4), 67-78 2022

Modelling determinants of antenatal care services utilization in Nigeria
AW Banjoko, MK Garba, RB Afolayan, NF Gatta
Kabale University Interdisciplinary Research Journal 1 (4), 4-13 2022

Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test
AW Banjoko, KO Abdulazeez
The Malaysian journal of medical sciences: MJMS 28 (5), 118 2021

On seemingly unrelated regression and single equation estimators under heteroscedastic error and non-Gaussian responses
RB Afolayan, AW Banjoko, MK Garba, WB Yahya
Journal of Engineering and Technology 5 (35) 2020

Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma.
AW Banjoko
Turkiye Klinikleri Journal of Biostatistics 12 (3) 2020

On the Use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company
MK Garba, AW Banjoko, WB Yahya, NF Gatta
Yeast (g) 4 (3.8760), 2.4631 2020

Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria
SB Akanni, MK Garba, AW Banjoko, RB Afolayan
2020

Weighted support vector machine algorithm for efficient classification and prediction of binary response data
AW Banjoko, WB Yahya, MK Garba, KO Abdulazeez
Journal of Physics: Conference Series 1366 (1), 012101 2019

Investigating the Effects of Multicollinearity on the Model Parameters of Ordinary Least Squares Estimator
NF Gatta, BA Waheed
International Journal of Advances in Scientific Research and Engineering 5 2019

Multiclass response feature selection and cancer tumour classification with support vector machine
AW Banjoko, WB Yahya, MK Garba
Journal of Biostatistics and Epidemiology 5 (2), 91-104 2019

ON THE STRENGTH OF AGREEMENT BETWEEN INITIAL AND FINAL ACADEMIC PERFORMANCES OF STUDENTS IN A NIGERIA UNIVERSITY SYSTEM
AW Banjoko, WB Yahya, HS Abiodun, RB Afolayan, MK Garba, ...
ABACUS (Mathematics Science Series) 45 (1) 2018

On the Strength of Agreement between Initial and Final Academic performances in a Nigerian University System
A Banjoko, W Yahya, HS Abiodun, R Afolayan, M Garba, KO Olorede, ...
ABACUS, Mathematical Association of Nigeria 2018

Sequential optimization based feature selection algorithm for efficient cancer classification and prediction
AW Banjoko, WB Yahya
4th iSTEAMS International Multidisciplinary Conference 14, 265-74 2018

Competing risk modeling using cumulative incidence function: Application to recurrent bladder cancer data
KA Dauda, WB Yahya, AW Banjoko, KO Olorede
FUOYE Journal of Engineering and Technology, Federal University of Science 2018

Partial Least Squares-Based Classification and Selection of Predictive Variables of Crimes against Properties in Nigeria
KO Olorede, WB Yahya, AO Garuba, AW Banjoko, KA Dauda
Edited Conference Proceedings of the 1st International Conference of the 2017

MOST CITED SCHOLAR PUBLICATIONS

Efficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samples
A.W. Banjoko, W. B. Yahya, M. K. Garba, O. R. Olaniran, K. A. Dauda and K. O ...
Annals. Computer Science Series. 13 (2), 69 - 79 2015
Citations: 15

IMPROVED BAYESIAN FEATURE SELECTION AND CLASSIFICATION METHODS USING BOOTSTRAP PRIOR TECHNIQUES
OR Olaniran, SF Olaniran, WB Yahya, AW Banjoko, MK Garba, LB Amusa, ...
Annals. Computer Science Series 14 (2) 2016
Citations: 13

SURVIVAL ANALYSIS WITH MULTIVARIATE ADAPTIVE REGRESSION SPLINES USING COX-SNELL RESIDUAL
KA Dauda, WB Yahya, AW Banjoko
Annals. Computer Science Series 13 (2), 25 - 41 2015
Citations: 10

On the Use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company
MK Garba, AW Banjoko, WB Yahya, NF Gatta
Yeast (g) 4 (3.8760), 2.4631 2020
Citations: 6

Multiclass response feature selection and cancer tumour classification with support vector machine
AW Banjoko, WB Yahya, MK Garba
Journal of Biostatistics and Epidemiology 5 (2), 91-104 2019
Citations: 6

Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test
AW Banjoko, KO Abdulazeez
The Malaysian journal of medical sciences: MJMS 28 (5), 118 2021
Citations: 5

Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria
SB Akanni, MK Garba, AW Banjoko, RB Afolayan
2020
Citations: 4

Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma.
AW Banjoko
Turkiye Klinikleri Journal of Biostatistics 12 (3) 2020
Citations: 3

A Test Procedure for Ordered Hypothesis of Population Proportions Against a Control.
WB Yahya, OR Olaniran, MK Garba, I Oloyede, AW Banjoko, KA Dauda, ...
Turkiye Klinikleri Journal of Biostatistics 8 (1) 2016
Citations: 2

Modelling determinants of antenatal care services utilization in Nigeria
AW Banjoko, MK Garba, RB Afolayan, NF Gatta
Kabale University Interdisciplinary Research Journal 1 (4), 4-13 2022
Citations: 1

The trade-off between the PLSR and PCR methods for modeling data with collinear structure
WB Yahya, KO Olorede, MK Garba, AW Banjoko, KA Dauda
Journal of the Nigerian Association of Mathematical Physics 39, 199-214 2017
Citations: 1