Detection of Schistosomiasis Factors Using Association Rule Mining

Abstract

Bilharzia or schistosomiasis is one of the most fatal and factitious disease happens through pollute which become a significant reason of deaths in the world. Prediction and factors identification that become causes of disease in early stage, may escort to treatment before it becomes critical. Data mining techniques are used to assist medical professionals effectively in diseases' classification. This research investigates the recovery and death factors which contributes to schistosomiasis disease preprocessed dataset, collected from Hubei, China. A computerized learning method, association rule mining (Apriori) is used to spot factors. Different tools were used for analysis and model evaluation with minimum support and minimum confidence indicated higher than 90% to generate rules. In addition, attributes indicating recovery and death of individuals were identified. Strong associations of disease factors; BMI, viability, nourishment, extent to ascites etc. determined and classified through Apriori algorithm. Further, results generated by association rule mining method may useful for professionals in treatment decision with better precision.

Keywords

• Schistosomiasis
• association rule mining (ARM)
• feature extraction
• prediction
• recovery
• death