Developing an Efficient Predictive Model Based on ML and DL Approaches to Detect Diabetes

Said Gadri


During the last decade, some important progress on machine learning ML area have been made, especially with the apparition of a new subfield called deep learning DL and CNN networks (Convolutional Neural Networks). This new tendency is used to perform much more sophisticated algorithms allowing high performance in many disciplines such as: pattern recognition, image classification, computer vision, as well as other supervised and unsupervised classification tasks. In this work, we have developed an automatic classifier that permits to classify a number of diabetic patients based on some blood characteristics by using ML and DL approaches. Initially, we have proceeded to the classification task using many ML algorithms. Then we proposed a simple CNN model composed of many layers. Finally, we established a comparison between ML and DL algorithms. For programming task, we have used Python, Tensorflow and Keras which are the most used in the field.

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