Waheed Babatunde Yahya

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Statistics, Probability and Uncertainty, Management Science and Operations Research

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



An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks
Morolake Oladayo Lawrence, Rasheed Gbenga Jimoh, and Waheed Babatunde Yahya
Springer Science and Business Media LLC



An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks
Morolake Oladayo Lawrence, Rasheed Gbenga Jimoh, and Waheed Babatunde Yahya
Springer Science and Business Media LLC



A new poisson-exponential-gamma distribution for modelling count data with applications
Waheed Babatunde Yahya and Muhammad Adamu Umar
Springer Science and Business Media LLC



BAYESIAN NON-INFERIORITY TEST BETWEEN TWO BINOMIAL PROPORTIONS




Hybridization of data-driven threshold algorithm with fuzzy particle swarm optimization technique for gene selection in microarray data
Paul Olujide Adebayo, Rasheed Gbenga Jimoh, and Waheed Babatunde Yahya
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



Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters
Yusuf Bello, Waheed B. Yahya, and Abdulrazaq AbdulRaheem
Nigerian Society of Physical Sciences
In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.


Determinants and spatial patterns of anaemia and haemoglobin concentration among pregnant women in Nigeria using structured additive regression models
Chinenye Pauline Ezenweke, Isaac Adeola Adeniyi, Waheed Babatunde Yahya, and Rhoda Enemona Onoja
Elsevier BV



Spatial variations and determinants of malnutrition among under-five children in Nigeria: A population-based cross-sectional study
Lateef Babatunde Amusa, Waheed Babatunde Yahya, and Annah Vimbai Bengesai
Public Library of Science (PLoS)
Childhood undernutrition is a major public health challenge in sub-Saharan Africa, particularly Nigeria. Determinants of child malnutrition may have substantial spatial heterogeneity. Failure to account for these small area spatial variations may cause child malnutrition intervention programs and policies to exclude some sub-populations and reduce the effectiveness of such interventions. This study uses the Composite Index of Anthropometric Failure (CIAF) and a geo-additive regression model to investigate Nigeria’s prevalence and risk factors of childhood undernutrition. The geo-additive model permits a flexible, joint estimation of linear, non-linear, and spatial effects of some risk factors on the nutritional status of under-five children in Nigeria. We draw on data from the most recent Nigeria Demographic and Health Survey (2018). While the socioeconomic and environmental determinants generally support literature findings, distinct spatial patterns were observed. In particular, we found CIAF hotspots in the northwestern and northeastern districts. Some child-related factors (Male gender: OR = 1.315; 95% Credible Interval (CrI): 1.205, 1.437) and having diarrhoea: OR = 1.256; 95% CrI: 1.098, 1.431) were associated with higher odds of CIAF. Regarding household and maternal characteristics, media exposure was associated with lower odds of CIAF (OR = 0.858; 95% CrI: 0.777, 0.946). Obese maternal BMI was associated with lower odds of CIAF (OR = 0.691; 95% CrI: 0.621, 0.772), whereas, mothers classified as thin were associated with higher odds of CIAF (OR = 1.216; 95% CrI: 1.055, 1.411). Anthropometric failure is highly prevalent in Nigeria and spatially distributed. Therefore, localised interventions that aim to improve the nutritional status of under-five children should be considered to avoid the under-coverage of the regions that deserve more attention.


Performance analysis of supervised classification models on heart disease prediction
Ezekiel Adebayo Ogundepo and Waheed Babatunde Yahya
Springer Science and Business Media LLC



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




A new three-parameter weibull inverse rayleigh distribution: Theoretical development and applications
Adeyinka Solomon Ogunsanya, Waheed Babatunde Yahya, Taiwo Mobolaji Adegoke, Christiana Iluno, Oluwaseun R. Aderele, and Matthew Iwada Ekum
Horizon Research Publishing Co., Ltd.
In this work, a three-parameter Weibull Inverse Rayleigh (WIR) distribution is proposed. The new WIR distribution is an extension of a one-parameter Inverse Rayleigh distribution that incorporated a transformation of the Weibull distribution and Log-logistic as quantile function. The statistical properties such as quantile function, order statistic, monotone likelihood ratio property, hazard, reverse hazard functions, moments, skewness, kurtosis, and linear representation of the new proposed distribution were studied theoretically. The maximum likelihood estimators cannot be derived in an explicit form. So we employed the iterative procedure called Newton Raphson method to obtain the maximum likelihood estimators. The Bayes estimators for the scale and shape parameters for the WIR distribution under squared error, Linex, and Entropy loss functions are provided. The Bayes estimators cannot be obtained explicitly. Hence we adopted a numerical approximation method known as Lindley's approximation in other to obtain the Bayes estimators. Simulation procedures were adopted to see the effectiveness of different estimators. The applications of the new WIR distribution were demonstrated on three real-life data sets. Further results showed that the new WIR distribution performed credibly well when compared with five of the related existing skewed distributions. It was observed that the Bayesian estimates derived performs better than the classical method.


Generalized Self–Similar First Order Autoregressive Generator (GSFO–ARG) for Internet Traffic
Jumoke Popoola, Waheed Babatunde Yahya, Olusogo Popoola, and Oyebayo Ridwan Olaniran
International Academic Press
Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.
  


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.


Application of Ordinal Logistic Regression Model to Nutritional Status of the Under-Five Children Indexed by Weight-for-Height
Anthony Ekpo and Waheed Babatunde Yahya
Knowledge E
Background and aim: In this paper, we present results regarding the outcomes of some anthropometric, epidemiological and demographic factors on the nutritional status of the under-five children which were categorized into three ordinal groups of Severe Acute Malnutrition (SAM), Moderate Acute Malnutrition (MAM) and Global Acute Malnutrition (GAM) in Kazaure Local Government Area in Nigeria.
 Methods: An ordinal logistic model that depicted the log-odds in favour of GAM (normal) child was fitted to the data based on surveillance indexed by Weight-For-Height (WFH).
 Results:The results showed that the proportional odd of measuring the nutritional status of a child in a nutrition survey using the WFH index has the OR= 7.43 (95% CI, 4.717 to 11.705) times greater, with Wald (1) 2  =74.81, p<0.001, hence a statistically significant effect.
 Conclusion: Based on the results and summary of findings, it can be concluded that age is a major predictor of the nutrition status of a child in a nutritional study when the surveillance is based on WFH index unlike sex and measles that do not play a major role.


Effects of Collinearity on Cox Proportional Hazard Model with Time Dependent Coefficients: A Simulation Study
B. T. Babalola and W. B. Yahya
Knowledge E
Background: The Cox proportional hazard model has gained ground in Biostatistics and other related fields. It has been extended to capture different scenarios, part of which are violation of the proportionality of the hazards, presence of time dependent covariates and also time dependent co-efficients. This paper focuses on the behaviour of the Cox Model in relation to time coefficients in the presence of different levels of collinearity.
 Objectives: The objectives of this study are to examine the effects of collinearity on the estimates of time dependent co-effiecients in Cox proportional hazard model and to compare the estimates of the model for the logarithm and the square functions of time.
 Materials and methods: The Algorithm based on a binomial model was extended in order to incorporate the different correlation structures required for the study. The scaled Schoenfeld residuals plots revealed the behaviour of the estimated betas at different degrees of collinearity. Results and conclusions are based of outcome of simulation study performed only.
 Results: The estimated betas were compared to the true betas at the different level of collinearity in graphical pattern.
 Conclusion: The study shows that collinearity is a huge factor that influences the correctness of the estimates of the regressors within the framework of Cox model.


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


Bayesian hypothesis testing of two normal samples using bootstrap prior technique
Oyebayo Ridwan Olaniran and Waheed Babatunde Yahya
Wayne State University Library System



Modelling Immunization Coverage in Nigeria Using Bayesian Structured Additive Regression
Samson Babatunde Adebayo and Waheed Babatunde Yahya
Springer Netherlands



A note on ridge regression modeling techniques
W. B. Yahya and J. B. Olaifa

In this study, the techniques of ridge regression model as alternative to the classical ordinary least square (OLS) method in the presence of correlated predictors were investigated. One of the basic steps for fitting efficient ridge regression models require that the predictor variables be scaled to unit lengths or to have zero means and unit standard deviations prior to parameters’ estimations. This was meant to achieve stable and efficient estimates of the parameters in the presence of multicollinearity in the data. However, despite the benefits of this variable transformation on ridge estimators, many published works on ridge regression practically ignored it in their parameters’ estimations. This work therefore examined the impacts of scaled collinear predictor variables on ridge regression estimators. Various results from simulation studies underscored the practical importance of scaling the predictor variables while fitting ridge regression models. A real life data set on import activities in the French economy was employed to validate the results from the simulation studies.


K-SS: A sequential feature selection and prediction method in microarray study




Gender effects on physical reactions of health science students at first encounter with cadaver using Pearson Chi-Square test

RECENT SCHOLAR PUBLICATIONS

Efficient multiclass prediction using sliced inverse regression with covariance hybridisation: an application to degrees classification problem
KO Olorede, WB Yahya
International Journal of Data Science and Analytics, 1-10 2025



Notes on a modified exponential-Gamma distribution: Its properties and applications
I Suleman, NR Zakariyau, OA Oyegoke, WB Yahya, FM Amiru, MA Umar
Journal of Basics and Applied Sciences Research 3 (2), 12-26 2025



A multi-objective optimization algorithm for gene selection and classification in cancer study
AW Banjoko, WB Yahya, OR Olaniran
Applied Soft Computing 172, 112911 2025



An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks
MO Lawrence, RG Jimoh, WB Yahya
Multimedia Tools and Applications 84 (8), 4393-4434 2025



Analysis of Fertility Determinants and Regional Disparities in Nigeria using Geo-Additive Regression
S Jabaru, K Jimoh, W Yahya
Yemeni Journal for Medical Sciences 19 (1), 1-12 2025



A Comprehensive Model for Exploring Unexplored Predictors of Fertility among Nigerian Women
SO Jabaru, WB Yahya, K Jimoh
2024



A new poisson-exponential-gamma distribution for modelling count data with applications
WB Yahya, MA Umar
Quality & Quantity, 1-21 2024



Hybridization of data-driven threshold algorithm with fuzzy particle swarm optimization technique for gene selection in microarray data
PO Adebayo, RG Jimoh, WB Yahya
Scientific African 23, e02012 2024



Evaluation of panel data estimators under the unbalanced panel data for small data sizes occasioned by missingness
OP Balogun, WB Yahya, AA Issa
2024



BAYESIAN NON-INFERIORITY TEST BETWEEN TWO BINOMIAL PROPORTIONS
WB Yahya, CP Ezenweke, OR Olaniran, IA Adeniyi, K Jimoh, RB Afolayan, ...
Reliability: Theory & Applications 19 (3 (79)), 689-703 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



A New Generalized Gamma-Weibull Distribution with Applications to Time-to-event Data
KA Dauda, RK Lamidi, AA Dauda, WB Yahya
bioRxiv, 2023.11. 18.567670 2023



Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters
WB Yahya, Y Bello, A AbdulRaheem
Journal of the Nigerian Society of Physical Sciences, 1437-1437 2023



Determinants and spatial patterns of anaemia and haemoglobin concentration among pregnant women in Nigeria using structured additive regression models
CP Ezenweke, IA Adeniyi, WB Yahya, RE Onoja
Spatial and Spatio-temporal Epidemiology 45, 100578 2023



Determinants and Spatial Patterns of Anaemia and Haemoglobin Concentration among Pregnant Women in Nigeria Using Structured Additive Regression Models
IA Adeniyi, CP Ezenweke, WB Yahya, RE Onoja
2023



Spatial variations and determinants of malnutrition among under-five children in Nigeria: A population-based cross-sectional study
LB Amusa, WB Yahya, AV Bengesai
Plos one 18 (4), e0284270 2023



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



Performance analysis of supervised classification models on heart disease prediction
EA Ogundepo, WB Yahya
Innovations in Systems and Software Engineering 19 (1), 129-144 2023



Spatial distributions and risk factors of overweight and obesity among women in Nigeria using structured geo-additive regression models: Analysis of 2018 Nigeria demographic
CP Ezenweke, IA Adeniyi, HO Edogbanya, WB Yahya
FUDMA JOURNAL OF SCIENCES 6 (4), 112-124 2022



BAYESIAN: ON OVERCOMING NON-CONVERGENCE AND UNREALISTIC PARAMETER ESTIMATES IN ITEM RESPONSE MODELLING
OM Adetutu, WB Yahya, A AbdulRaheem
6th Annual International Conference of the Professional Statisticians 2022

MOST CITED SCHOLAR PUBLICATIONS

Handbook of statistics in clinical oncology
J Crowley, A Hoering
CRC Press 2012
Citations: 259



Effects of non-orthogonality on the efficiency of seemingly unrelated regression (SUR) models
WB Yahya, SB Adebayo, ET Jolayemi, BA Oyejola, OOM Sanni
InterStat Journal 1, 1-29 2008
Citations: 47



Modelling the trend and determinants of breastfeeding initiation in Nigeria
WB Yahya, SB Adebayo
Child Development Research 2013 (1), 530396 2013
Citations: 46



Profit maximization in a product mix company using linear programming
WB Yahya, MK Garba, SO Ige, AE Adeyosoye
European Journal of Business and management 4 (17), 126-131 2012
Citations: 37



K-SS: A sequential feature selection and prediction method in microarray study
WB Yahya, K Ulm, F Ludwig, A Hapflemeir
International Journal of Artificial Intelligence 6 (S11), 19-47 2011
Citations: 33



Performance analysis of supervised classification models on heart disease prediction
EA Ogundepo, WB Yahya
Innovations in Systems and Software Engineering 19 (1), 129-144 2023
Citations: 30



Exploring some properties of odd Lomax-exponential distribution
AS Ogunsanya, OO Sanni, WB Yahya
Annals of Statistical Theory and Applications (ASTA) 1, 21-30 2019
Citations: 30



Bayesian hypothesis testing of two normal samples using bootstrap prior technique
OR Olaniran, WB Yahya
Journal of Modern Applied Statistical Methods 16 (2), 34 2017
Citations: 29



A comparison of some test statistics for multivariate analysis of variance model with non-normal responses
BL Adeleke, WB Yahya, A Usman
Journal of Natural Sciences Research 5 (15), 1-9 2015
Citations: 25



On Bayesian conjugate normal linear regression and ordinary least square regression methods: A Monte Carlo study
WB Yahya, OR Olaniran, SO Ige
Ilorin Journal of science 1 (1), 216–227-216–227 2014
Citations: 23



Investigations of certain estimators for modelling panel data under violations of some basic assumptions
MK Garba, BA Oyejola, WB Yahya
Mathematical Theory and Modeling 3 (10), 47-53 2013
Citations: 22



A new three-parameter weibull inverse rayleigh distribution: theoretical development and applications
AS Ogunsanya, WB Yahya, TM Adegoke, C Iluno, OR Aderele, MI Ekum
Mathematics and Statistics 9 (3), 249-272 2021
Citations: 18



Microarray-based Classification of Histopathologic Responses of Locally Advanced Rectal Carcinomas to Neoadjuvant Radiochemotherapy Treatment
KULM Waheed Babatunde YAHYA, Robert ROSENBERG
Turkiye Klinikleri J Biostat 6 (1), 8-23 2014
Citations: 18



26 Predictive modeling of gene expression data
A Hapfelmeier, W Babatunde, RR Yahya, K Ulm
Handb Stat Clin Oncol 4, 71 2012
Citations: 18



Determination of optimum product mix at minimum raw material cost, using linear programming
WB Yahya
Nigeria Journal of Pure and Applied Sciences 19 (2), 1712-1721 2004
Citations: 18



Efficient support vector machine classification of diffuse large b-cell lymphoma and follicular lymphoma mRNA tissue samples
AW Banjoko, WB Yahya, MK Garba, OR Olaniran, KA Dauda, KO Olorede
Faculty of Computer and Applied Computer Science, Tibiscus University of 2015
Citations: 15



A note on ridge regression modeling techniques
WB Yahya, JB Olaifa
Electronic Journal of Applied Statistical Analysis 7 (02), 343-361 2014
Citations: 15



A fast algorithm to construct neural networks classification models with high-dimensional genomic data
WB Yahya, MO Oladiipo, ET Jolayemi
Annals. Computer Science Series 10 (1), 39-58 2012
Citations: 14



Improved Bayesian feature selection and classification methods using bootstrap prior techniques
OR Olaniran, SF Olaniran, WB Yahya, AW Banjoko, MK Garba, LB Amusa, ...
Faculty of Computer and Applied Computer Science, Tibiscus University of 2016
Citations: 13



Spatial variations and determinants of malnutrition among under-five children in Nigeria: A population-based cross-sectional study
LB Amusa, WB Yahya, AV Bengesai
Plos one 18 (4), e0284270 2023
Citations: 12