Automated Classification Method for Early Diagnosis of Alopecia Using Machine Learning

Abstract

Artificial neural networks are being used in many fields for different purposes. Medical diagnosis is one of the major purposes. In the field of medical, classification plays an important role as the main aim of the doctor is to classify whether a person is suffering from the disease or not. The objective of this paper is to evaluate neural network for the detection of alopecia in human beings and to find the accuracy. With the help of proposed model, the clinical experts will be able to get a second opinion that will help them to take proper decisions for diagnosing the presence of this disease in patients. This second opinion is crucial due to many factors while doing disease identification. These factors include increased population, environmental pollution, growing demands for proper medication, and less availability of medical experts to cope up with this increasing demand. Also, the dynamic nature of disease symptoms plays an important role in correct diagnosis of a certain disease. The proposed system uses a feedforward artificial neural network and backpropagation algorithm to classify patients with alopecia and without alopecia. The evaluation of the proposed system is done with the help of performance plot as well as regression plot. Experimental results show that the accuracy of proposed system is 91% which is reliable enough for a clinical expert to make his decisions.