Exploration of Machine Learning Methods in Medical Disease Prediction: A Systematic Literature Review
DOI:
https://doi.org/10.59247/jahir.v1i3.174Keywords:
Machine Learning, Medical, Diagnose, Prediction, DiseaseAbstract
Exploration of Machine Learning methods in the systematic literature shows successful applications in disease diagnosis, disease prediction, and treatment planning. This literature only includes discussions on Classification methods consisting of Support Vector Machine(SVM), Naïve Bayes, Nearest Neighbors and Neural Network(NN) and Regression consisting of Decision Tree, Linear Regression, Random Forest Ensemble Methods, and Neural Network(NN). Clustering which consists of K-Means Clustering, Artificial Neural Network (ANN), Gaussian Mixture, Neural Network (NN) and Dimensionality reduction which consists of Principal Component Analysis (PCA). In the context of healthcare, the importance of sustainability, ethics, and data security are key factors. This research uses Systematic Literature Review (SLR) to explore Machine Learning methods in the medical context and recommends Support Vector Machine, Random Forest, and Neural Networks as effective methods. By exploring 300 papers and selecting 57 papers for discussion of machine learning methods in medical disease prediction. Method selection should be tailored to the dataset characteristics and disease prediction goals, while prioritizing
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