Machine Learning (ML), a branch of Artificial Intelligence (AI), has been successfully applied in healthcare domain to diagnosing diseases. The ML techniques have not only been able to diagnose the common diseases but are also equally capable of diagnosing the rare diseases. Although ML offers systematic and sophisticated algorithms of multi-dimensional clinical data, the accuracy of the ML in diagnosing the diseases is still a concern. And the improvement in the performance of ML to diagnose disease is a hot topic in this domain. As different ML approach perform differently for different healthcare dataset, we need an approach to apply multiple state of art algorithms in reasonable time using few lines of codes, so that the search of best ML method to diagnose a particular disease can be pursued efficiently. In our work we show that, the use of libraries like AutoGluon can help to find the best performing ML approach out of many approaches in diagnosing the disease for a given dataset in reasonable time and with optimal lines of code. 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 ML approaches like Naïve Bayes, Support Vector Machine (SVD), K Nearest Neighbors (KNN), perceptron and robust deep neural networks in AutoGluon like LightGBM, XGBoost, MXNet etc. based on a public diabetes dataset.