Browsing by Author "Abdulsalam, Sulaiman Olaniyi"

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    A chi-square-SVM based pedagogical rule extraction method for microarray data analysis
    (Institute of Advanced Engineering and Science (IAES), 2020) Salawu, Mukhtar Damola; Arowolo, Micheal Olaolu; Abdulsalam, Sulaiman Olaniyi; Isiaka, Rafiu Mope; Jimada-Ojuolape, Bilkisu; Olumide, Mudashiru Lateef; Gbolagade, Kazeem A.
    Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
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    A KNN and ANN model for predicting heart diseases
    (Explainable Artificial Intelligence in Medical Decision Support Systems, 2024-07-03) Abdulsalam, Sulaiman Olaniyi; Arowolo, Micheal Olaolu; Udofot, Enobong Chidera; Sanni, Ayodeji Matthew; Popoola, Damilola David; Adebiyi, Marion Olubunmi
    The heart is the single most important organ in the human body. Patients, professions, and medical systems are all bearing the brunt of heart failure’s devastating effects on contemporary society. Since cardiac arrest may well be demonstrated as a better understanding or conceivably go unobserved, particularly in the vast population of clients that have other cardiovascular disorders, the true prevalence of heart failure is likely to be underestimated, accounting for only 1–4% of all hospitalized patients as test procedures in developed nations.A person with heart failure has a heart that is unable to circulate sufficient blood through the body, but the term“heart failure” does not explain why this happens. The clinical picture is confusing since there are several possible causes of heart problems, many of which are diseases in and of themselves. Many cases of heart failure can be avoided if the underlying medical conditions that cause them are identified and treated promptly. The study and prediction of cardiac conditions must be precise because numerous diseases have been connected to the cardiovascular system. The resolution of this problem requires intensive online research on the relevant topic. Since incorrect illness prognoses are a leading cause of death among heart patients, learning more about effective prediction algorithms is crucial. This research utilizes K-nearest neighbor (KNN) and artificial neural network (ANN) to assess cardiovascular diseases using data collected from Kaggle. The highest accuracy (96%) was achieved by ANN trained with the standard scalar. Medical experts, specialists, and academics can all benefit greatly from this study. Based on the results of this study, cardiologists will be able to make more knowledgeable decisions about the inhibition, analysis, and handling of heart disease
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    Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms
    (International Journal of Multidisciplinary Sciences and Advanced Technology, 2020) Abdulsalam, Sulaiman Olaniyi; Arowolo, Micheal Olaolu; Jimada-Ojuolape, Bilkisu; Saheed, Yakub Kayode
    This study proposes a customer churn mining structure based on data mining methods in a banking sector. This study predicts the behavior of customers by using clustering technique to analyze customer’s competence and continuity with the sector using k-means clustering algorithm. The data is clustered into 3 labels, on the basis of the transaction in and outflow. The clustering results were classified using Support Vector Machine (SVM), an Accuracy of 97% was achieved. This study enables the banking administrators to mine the conduct of their customers and may prompt proper strategies as per engaging quality and improve proper conducts of administrator capacities in customer relationship.