A Hybrid Expert Tool for the Diagnosis of Depression

Diagnoses of psychiatric diseases are still a major issue. Two key reasons are—there are variations in the opinions of the medical doctors and the presentation of a disease among the subjects. Given this scenario, the focus of this paper is to develop a hybrid approach for diagnosing adult depression (302 × 16), where each case is represented with 15 symptoms [0, 1] and the corresponding target (that is, grade-probability as 'mild,' 'moderate,' and 'severe'). The proposed hybrid tool is consisted of (a) information gain measure of each symptom/attribute to find the dominant symptoms and (b) a multilayer feedforward-backpropagation neural network, which has been fed with the (i) dominant symptoms and (ii) all symptoms as the inputs to compare its performance. The study observes that with dominant symptoms, the tool is able to classify depression with 98.96% average accuracy, compared to all symptoms, where the average accuracy is 98.91%. The paper concludes that attribute selection procedure based on decision tree learning has increased the efficiency of the tool.