Mining Sequential Risk Patterns for Early Assessment of COPD

Abstract

Nowadays, chronic diseases have been among the major concerns in medical fields since they may cause heavy burden on healthcare resources and disturb the quality of life. Chronic Obstructive Pulmonary Disease (COPD) is one kind of popular chronic diseases. COPD takes long period to evolve from mild symptoms (Stage I) to severe illness (Stage IV) and death. Earlier the disease is detected, the better the scope for effective treatment and improved control of symptom development. Therefore, the early of COPD is beneficial for better treatment. The paper examines the novel system for early appraisal on chronic illnesses by mining sequential risk patterns with interim data from diagnostic clinical records utilizing sequential rules mining and classification modelling systems. The system consists of four phases namely data pre-processing, risk pattern mining, classification modelling. SPADE algorithm and CBS algorithm used for risk pattern mining and classification. Decision tree algorithm is compared with the SPADE algorithm, and SPADE showing a better accuracy when comparing with decision tree.