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An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

Kanimozhi, U., Ganapathy, S., Manjula, D., Kannan, A.
National Academy science letters 2019 v.42 no.3 pp. 227-232
artificial intelligence, breast neoplasms, data collection, health services, models, prediction, questionnaires, risk
Online prediction of risk on breast cancer is a challenging task in the area of health care during the past decade. Since the existing statistical and data mining methods have limitations with respect to the prediction of breast cancer, there is a need for proposing more effective predictive models which can predict the breast cancer more effectively. In this paper, we propose a new intelligent online risk prediction model for predicting the breast cancer using fuzzy temporal rules more accurately. Moreover, this intelligent system determines the contributing attributes from the dataset using intelligent fuzzy temporal rules and also performs prediction by applying fuzzy rule-based classification with temporal constraints. Moreover, the rules are validated using a domain expert and the experiments conducted in this work using questionnaire, rule-based classification and consultation with domain expert have proved that the proposed system provides more accurate results for risk prediction than the other existing systems.