A DNN Model for Diabetes Mellitus Prediction on PIMA Dataset
Main Article Content
There are several deadly diseases that have affected the common man around the globe irrespective of region and the most common among them is diabetes mellitus (DM). To predict the onset of the disease, deep learning can play a vital role. However, the data fed to the deep learning algorithm need to be free from various anomalies like outliers, missing values and inappropriate attributes etc. To enhance the data and make it adequately appropriate for the decision-making process, various pre-processing techniques are available. In this paper, a Deep Neural Network (DNN) model is developed and the effect of pre-processing techniques is shown by comparing the results of applying various pre-processing techniques to handle missing values to the PIMA dataset. The results show that the model performs better using Mean while applying Minmax and Robust scalar techniques.
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