Applying decision tree and neural network to increase quality of dermatologic diagnosis

Authors:
Chun-Lang Chang;Chih-Hao Chen
Affiliations:
Institute of Industrial Engineering and Management, National Formosa University, 64 Wun-Hwa Road, Huwei, Yunlin 632, Taiwan, ROC;Institute of Industrial Engineering and Management, National Formosa University, 64 Wun-Hwa Road, Huwei, Yunlin 632, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Skin diseases are common to children and adults. Many factors influence the onsets of these diseases, and each age group usually has its different symptoms. In the humid, damp, and hot weather conditions of Taiwan, bacteria and molds grow best and fast. Also, exposures to excess amounts of ultraviolet radiations in the sunlight will make skin sensitive, easy to be infected, and possibly cause skin problems. In addition to the external infections, internal sebaceous glands, dead skin, sweats, mixed with dusts and other unwanted secretions can cause other serious skin diseases. Although skin diseases are easier to detect, and diagnosing symptoms and deciding treatment plans are not as complex as other internal diseases, many people often ignore the importance of them. In fact, even a small spot on the skin might cause skin cancer. This study conducted five experiments focusing on six major skin diseases as its research subjects. It uses decision tree of data mining combining with neural network classification methods to construct the best predictive model in dermatology. The results show that using neural network model has the highest, 92.62%, accuracy in prediction. Using sensitivity analysis combining with decision tree model, on the contrary, has the least accuracy, which is 80.33%. Based on this result, the AI classification technology can serve as important and useful references in diagnosis for physicians to avoid unnecessary medical waste and enhance health care quality.