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Olasupo, Yinusa Ademola
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Computer Science & IT

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Enhancing Credit Card Fraud Detection with Modified Binary Bat Algorithm: A Comparative Study with SVM, RF, and DT Olasupo, Yinusa Ademola; Malgwi, Musa Yusuf; Hambali, Moshood Abiola
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Numerous studies have revealed the problem of irrelevant features, noise, and dimensionality in a dataset, which can inhibit how the classification algorithm performs. In machine learning, feature selection approaches are critical, particularly in the context of credit card fraud detection, where relevant feature selection is critical. We use techniques such as machine learning algorithms, data mining techniques, and data science to stop and detect credit card fraud. These algorithms often classify genuine and fraudulent transactions in credit card datasets. However, the challenge of high dimensionality and irrelevant features persists, hindering improvements in classifier algorithms. This study centered on detecting credit card fraud (CCF) using a Modified Binary Bat Algorithm (MBBA) for feature selection. The MBBA selects the most informative features to improve the classifier algorithm's performance. The classifier algorithms used in this research are Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). We conducted the experiment using the Python programming language, and the results indicate that RF achieves 99.945% accuracy, SVM 99.847%, and DT 99.909%. As a result, RF has the best accuracy. In summary, the optimal performance of a classification algorithm depends on the selection of relevant features for credit card fraud detection. The paper suggests improving the effectiveness of classifier algorithms for credit card fraud detection by employing the Modified Binary Bat algorithm, which outperforms the Genetic Algorithm (GA) in feature selection
Heart Disease Prediction Using Principal Component Analysis and Decision Tree Algorithm Hambali, Moshood Abiola; Gbolagade, Morufat Damola; Olasupo, Yinusa Ademola
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.617

Abstract

Globally, cardiovascular disease is among the major diseases that lead to death. Early forecasts are crucial. Using the patient's medical record, the supervised learning algorithm for predicting heart disease at an early stage was proposed. The principal component analysis (PCA) classifier and decision tree algorithm were created to classify medical record data. To predict cardiovascular diseases, data mining was utilized. The proposed strategy improves the diagnostic efficiency of physicians. Using data received from the UCI repository, the classifier's efficacy was confirmed. PCA offers 98% precision, 100% sensitivity, and 98% accuracy. In terms of accuracy, sensitivity, and precision, the results showed that the PCA outperformed the decision tree. 
Co-Authors Gbolagade, Morufat Damola Hambali, Moshood Abiola Malgwi, Musa Yusuf