IoT enabled healthcare system for predicting the diseases using feature optimization, decision tree, neural network, and fuzzy temporal rules

Abstract

SummaryDiabetes, cancer, and heart diseases are identified to be fatal for human beings due to the lack of awareness, erratic food habits, lack of physical work and so forth. Many disease prediction systems are introduced by various researchers. However, early diagnosis of the diseases and monitoring systems are not working effectively and it increases the death rate. Recently, Internet of Things (IoT) is incorporated to collect the instant data from patient and the system makes effective decisions. Many IoT enabled healthcare systems are available for predicting the specific diseases but such effective healthcare system for measuring the severity of diabetes, cancer and heart diseases is scarce. For this purpose, a new neuro‐fuzzy model is used in the proposed IoT enabled healthcare system for disease prediction, which combines fuzzy rules with temporal features. First, it categorizes the available features of the patient records. Second, a new temporal decision tree is introduced for learning the patient records carefully and predicting the record by using the available data. Third, the fuzzy temporal decision tree calculates the relationship between the available features and also selects them as contributed features. In addition, some of the features are identified as not useful for making a final decision, and these are to be removed. Fourth, the fuzzy rules are to be generated by using the Mamdani fuzzy membership function. Finally, a new framework is proposed which is the combination of neuro‐fuzzy temporal networks, fuzzy temporal decision trees, and fuzzy temporal rules. The proposed model is evaluated by conducting experiments and proved as better than the existing systems in terms of prediction accuracy, error rate, and efficiency.