A Study of Disease Diagnosis Using Machine Learning

Abstract

Machine Learning (ML), a branch of Artificial Intelligence (AI), has been successfully applied in the healthcare domain to diagnosing diseases. The ML techniques have not only been able to diagnose common diseases but are also equally capable of diagnosing rare diseases. Although ML offers systematic and sophisticated algorithms for multi-dimensional clinical data, the accuracy of ML in diagnosing diseases is still a concern. As different ML approaches perform differently for different healthcare datasets, we need an approach to apply multiple state of art algorithms with optimal lines of codes, so that the search for the best ML method to diagnose a particular disease can be pursued efficiently. In our work, we show that, the use of libraries such as AutoGluon can be used to compare the performances of multiple ML approaches to diagnosing a disease for a given dataset with a couple of lines of codes. This will decrease the probability of inaccurate diagnosis, which is a significantly important consideration while dealing with the health of the people. We have tested the performance of 20 ML approaches such as Naïve Bayes, Support Vector Machine (SVD), K Nearest Neighbors (KNN), perceptron, and robust deep neural networks in AutoGluon such as LightGBM, XGBoost, MXNet, etc., based on the Pima Indian Diabetes Dataset.