Moussa Mahamat Boukar

Faculty of natural and applied Sciences, Department of Computer Science, Nile University of Nigeria, Nigeria


  • Research   
    Machine Learning Approaches for Optimal Parameter Selection for Hepatitis Disease Classification
    Author(s): Rukayya Umar, Moussa Mahamat Boukar, Steve Adeshina and Senol Dane*

    Background: In the field of medicine, diagnosis is very important issue. In effort to address the issue Machine learning is being utilized for model development for classification and diagnosis purposes. Feature selection (course of dimensionality reduction) is an important component of ML to increase model performance by reducing redundant features which may degrade model performance accuracy. Objective: The goal of this work is to identify among three feature selection approaches which one gives optimal solution (subset of features) that when used for classification provides the best fit performance accuracy and minimized CPU time. Method: using newly created subsets of features we develop model M for hepatitis disease classification for dataset D (xi,yi) where subsets {x1…,xn} contribute to target variable as the original features do. Chi-square, Genetic Algorithm and Princi.. Read More»

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