Informatics Publishing Limited

Kawsar Ahmed ¹, Tasnuba Jesmin ¹, Ushin Fatima ², Md. Moniruzzaman ², Abdulla-Al-Emran ³, Md. Zamilur Rahman ¹

Affiliations
1 Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, BD
2 Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, BD
3 Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902q, BD

Abstract

Diabetes is not only a disease but also responsible for occurring different kinds of diseases such as heart attack, kidney disease, blindness and renal failure. With respect to Bangladesh, Diabetes is a deadly, disabling and cost disease whose risk is increasing at alarming rate. The diagnosis of diabetes is a vital and tedious task. The detection of diabetes from some important risk factors is a multi-layered problem. Initially 400 diabetes and non-diabetes patients’ data is collected from different diagnostic centre and data is pre-processed. After pre-processing data is clustered using K-means clustering algorithm for identifying relevant and non-relevant data to diabetes. Next significant frequent patterns are discovered using AprioriTid shown in Table 1 and Decision Tree algorithm shown in Table 2. Finally implement a system to predict diabetes which is easier, cost reducible and time saveable.

Keywords

Data Pre-Processing, Data Classification, Aprioritid Algorithm, DT (Decision Tree) Algorithm, K-Means Clustering, Significant Frequent Pattern.

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