Browsing by Author "Raihan Uddin"

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    A Comparative Analysis of Machine Learning Models for Liver Disease Prediction
    (2024-05-13) Raihan Uddin
    Liver disease is a widespread global health concern that demands early detection for effective treatment and improved patient outcomes. Traditional diagnostic methods have their limitations, necessitating further exploration into the use of machine learning to enhance predictive accuracy, accessibility, and cost-effectiveness in liver disease diagnosis. This study provides an in-depth analysis of machine learning classifiers such as Support Vector Machine, K-Nearest Neighbors, Decision Tree, Multilayer Perceptron, and Random Forest for liver disease prediction and compares their performance. The primary objective of this research is to identify the most effective machine-learning classifier for precise and accessible liver disease prediction. This research has the potential to significantly impact patient care, as it can aid in the development of efficient diagnostic tools, reduce the global burden of liver disease, and improve patient outcomes. This study's robust methodology includes feature selection via the Pearson Correlation matrix, leveraging the 10-fold cross-validation technique, and a balanced dataset generated using the Synthetic Minority Over-sampling Technique (SMOTE) on our collected dataset from distinct hospitals across Bangladesh. Our approach, employing a Random Forest classifier, achieved an impressive 80.5 percent accuracy in liver disease prediction. Furthermore, our model demonstrated a precision of 0.88 and a recall of 0.85, indicating its robust performance in identifying positive and negative cases. Keywords: Liver Disease, Pearson Correlation, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Multilayer Perceptron.