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An Empirical Study of Applying Artificial Neural Network for Classification of Dermatology Disease
Background/Objectives: With the growth in complexity and volume of medical data, an extensive set of information currently available in various forms related to diseases and its symptoms. Mechanisms are necessary to extract rules and patterns from these massive set of data. Identification and extraction of hidden patterns and rules in this massive data set certainly help us to understand about diseases progression facts. Methods: Machine learning provides an automatic way to uncover the patterns from data set and it will be helpful to health care professionals in order to provide precision medicine to their patients. Artificial Neural Network is a popular machine learning technique used for classification tasks in medical diagnosis for diseases detection. It is an eminent field of computer science which can be applied to the health care sector quite efficiently. In this study, Multi-Layer Feed Forward Neural Network has been applied to the dermatology dataset downloaded from UCI repository site to classify the dermatology diseases. Findings: Artificial Neural Network with back propagation algorithm produces the optimum results for classification and prediction problems. It also possesses the ability of generalization and applicable to real world problem. Applications: The experiment will be extended by applying on other types of diseases datasets and an automated diagnostic and advisory system with neural network integration definitely helps in diseases prediction problem.
Artificial Neural Network, Classification, Disease Diagnosis
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