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

During the last decade, some important progress in machine learning ML area has 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 the classification of a large 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 DNN model composed of many layers. Finally, we established a comparison between ML and DL algorithms, as well as our model with other existing models. For the programming task, we have used Python, Tensorflow, and Keras which are the most used in the field. [ABSTRACT FROM AUTHOR]

V tem delu smo razvili avtomatski klasifikator, ki omogoča klasifikacijo več bolnikov s sladkorno boleznijo na podlagi nekaterih značilnosti krvi z uporabo ML in DL pristopov. [ABSTRACT FROM AUTHOR]