Browsing by Author "Ajao, J.F."

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    A Machine Learning Approach to Dropout Early Warning System Modeling
    (International Association of Academicians - International Journal of Advanced Studies in Computer Science and Engineering, 8(2): 1 – 12, 2019) Isiaka, R.M.; Babatunde, R.S.; Ajao, J.F.; Abdulsalam, S.O.
    ABSTRACT Dropout has been identified as a social problem with an immediate and long term consequences on the socio economic development of the society. A major element of a conceptual framework for dropout monitoring and control system is the prevention component. The component specified the need for automatic dropout early warning system. This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Historical dataset from a State University in the North Central region of Nigeria was used for building and testing the adaptive model. The average performance of the model on full attributes and the first year academic attributes compared favourably with 0.98 Precision, Recall and F1-Scores. Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factorfor dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school managementtowards curtailing the menace. Keywords: dropout, adaptive-model, data mining, machine learning prediction
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    Classification of Leukemia Cancer Data using Correlation Based Feature Selection Model: A Comparative Approach
    (Faculty of Engineering, Osun State University, Osogbo, Nigeria - UNIOSUN Journal of Engineering and Environmental Sciences, 2025) Babatunde, R.S.; Isiaka, R.M.; Abdulsalam, S.O.; Arowolo, O.M.; Ajao, J.F.
    The abundance of data obtained from microarray experiments presents challenges related to the number of variables and the presence of random fluctuations. Despite the efforts that had been made by previous researchers, emphasizing how data mining aids the implementation of models to facilitate informed prediction, gaps are evident which requires improvement over the earlier models. Dimensionality reduction techniques, such as Correlation Based Feature Selection (CBFS), are good candidate solutions to these problems by selecting pertinent features for categorization. This research implements a model for classification of leukemia cancer using CBFS with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Ensemble classifiers. The evaluation of the performance of these machine learning models was carried out using sensitivity, specificity, precision and accuracy. The findings indicate that the CBFS+DT model outperforms the other models in terms of sensitivity (96.75%), specificity (97.18%), precision (97.56%), accuracy (96.75%), and F1 score (96.97%), while also exhibiting a decreased computational time (0.4336). This demonstrates the efficacy of CBFS in improving classification accuracy and reducing computing load. Overall, this study highlights the effectiveness of CBFS in cancer research and underscores the importance of carefully choosing the most pertinent variables to enhance classification outcomes. Keywords: machine learning, classification, feature selection, pattern recognition
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    Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
    (Springer Nature Switzerland, Lecture Notes in Business Information Processing 402: 480 – 492 - Proceedings of 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, 2020) Abdulsalam, S.O.; Abubakar, A.M.; Ajao, J.F.; Babatunde, R.S.; Ogundokun, R.O.; Nnodim, C.T.; Arowolo, M.O.
    A significant application of microarray gene expression data is the classification and prediction of biological models. An essential component of data analysis is dimension reduction. This study presents a comparison study on a reduced data using Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) feature selection dimension reduction techniques, and evaluates the relative performance evaluation of classification procedures of Support Vector Machine (SVM) classification technique. In this study, an accuracy and computational performance metrics of the processes were carried out on a microarray colon cancer dataset for classification, SVM-RFE achieved 93% compared to ANOVA with 87% accuracy in the classification output result. Keywords: SVM-RFE; ANOVA; Microarray; SVM; Cancer