Machine learning based prediction of depression among type 2 diabetic patients

Abstract

Most of humankind feel sadness, tragic, feeling down from time to time; a few people encounter these emotions strongly, for long period of time and usually with no evident reason. Depression is not a low mood only; it's a genuine condition that affects the physical and mental health of the human. There are many studies that demonstrate a close association between depression and type 2 diabetes. Therefore, this paper aims to consolidate prediction of depression operation through the developing and applying the machine learning techniques. The supervised machine learning aims to construct a compact model of the allocation of class labels based on set of features to mimic the reality. The classification technique is used to give class labels to the subjects under testing based on values of the known prediction features, but the class label is unknown. In this paper state of art supervised learning classifiers have been used with modification to the used data. The results are very encouraging to use machine learning in the Prediction of Depression among Type 2 Diabetic Patients.

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