Title: Data mining techniques and fuzzy logic to build a risk prediction system for stroke

Authors: Farzana Islam; Rashedur M. Rahman

Addresses: Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara, Dhaka 1229, Bangladesh ' Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara, Dhaka 1229, Bangladesh

Abstract: Nowadays, by using different computational systems medical sector predicts diseases. These systems not only aid medical experts but also normal people. In recent years stroke becomes life threatening deadly cause and it increased at global alarming state. Early detection of stroke disease can be helpful to make decision and to change the lifestyle of people who are at high risk. There is a high demand to use computational expertise for prognosis stroke. Research has been attempted to make early prediction of stroke by using data mining techniques. This paper proposes rule based classifier along with other techniques. The dataset is collected from Dhaka medical college, situated in Dhaka, Bangladesh. To build a more accurate and acceptable model the system uses different classification methods likely- decision tree, support vector machine (SVM), artificial neural network (ANN) and fuzzy model. K-means, EM and fuzzy C-means clustering (FCM) algorithm are used to label the dataset more accurately. Fuzzy inference system is also built to generate rules. Adaptive neuro fuzzy inference system (ANFIS) provides the most accurate model.

Keywords: stroke; decision tree; SVM; support vector machine; MLP; multilayer perceptron; ANN; artificial neural network; support vector machine; fuzzy model; FIS; ANFIS; adaptive neuro fuzzy inference system; data-mining; FCM; fuzzy C-means clustering; clustering; EM clustering; k-means; Bangladeshi dataset; fuzzy rule.

DOI: 10.1504/IJAIP.2026.154542

International Journal of Advanced Intelligence Paradigms, 2026 Vol.31 No.1/2, pp.19 - 39

Received: 16 Apr 2018
Accepted: 08 Jun 2019
Published online: 03 Jul 2026 *

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