COMPARING MACHINE LEARNING MODELS FOR DISEASE PREDICTION
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UNIVERSITI MALAYSIA SARAWAK
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AI has been widely applied in many fields, including healthcare, where it helps to analyze data and make predictions. Machine learning is often used to solve problems by analyzing patterns in data, and it helps create predictive models in fields like healthcare. This research focuses on comparing different machine learning algorithms for disease prediction using symptoms as input data. Disease prediction helps in early diagnosis and timely treatment, which can improve patient outcomes and reduce healthcare costs. The algorithms studied include Support Vector Machines (SVM), Decision Tree, Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost). SVM is effective in handling high-dimensional data and performs well with smaller datasets. Decision Tree is easy to understand and interpret, making it useful for simple decision-making processes. Random Forest is a collection of decision trees that improves accuracy and reduces overfitting. Logistic Regression is often used for binary classification tasks and provides straightforward results. XGBoost is a boosting algorithm that combines predictions from multiple models and performs well on structured datasets. Each algorithm has its strengths and weaknesses, and this study aims to find the one that balances performance, interpretability, and computational efficiency. The results will guide the selection of the best algorithm for deployment in a user-friendly tool that can help users understand the likelihood of specific diseases based on their symptoms. This study contributes to the growing field of AI applications in healthcare and highlights how machine learning can simplify complex diagnostic processes.