Machine Learning Techniques for the Management of Diseases: A Paper Review

The advancement in Artificial Intelligence has led to the improvement in human lives. Machine learning algorithms in particular and Artificial Intelligence in general have become very useful in today’s activities. One of the sectors that has benefited from the new technology is the health sector. Machine learning techniques have been useful in the diagnosis and prediction of rare diseases. Many health sectors are using the techniques for the diagnosis and prediction of diseases thereby improving on the health situations of the patients in record time. Artificial Intelligence-based methods help in reducing the doctor to patients’ ratio gap by providing machine learning alternatives in the prediction of diseases. In this paper, we give an overview of different machine learning techniques and their relevance in the diagnosis of particular diseases. Machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes, K Nearest Neighbor (KNN),Decision Tree (DT),Random Forest (RF), Artificial neural network (ANN), Convolution Neural Network (CNN), Logistic Regression and Linear Regression used for the diagnosis of diseases have been reviewed. A collection of most non-communicable diseases diagnosed using machine learning has been examined. A comparative analysis of the accuracy performance to diagnose diseases with different machine learning algorithms has also been presented.