Preterm Birth Prediction Using Cuckoo Search-Based Fuzzy Min-Max Neural Network


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Abstract


In the latest history, a Decision making and prediction system has been investigated vigorously for several decades and has got a lift. Together with preterm birth study, the decision support system has been explored in different areas. Using fuzzy, neural network and cuckoo search algorithm, the medical decision support system is improved for the forecast of preterm birth in this document. A two-module pattern categorization and rule extraction system has been highlighted by this study, where in the former module emphasises an altered fuzzy min–max (FMM) neural-network-based pattern classifier, whereas the subsequent module emphasises oppositional cuckoo search based rule extractor. With the theory of opposition, this paper examines altered cuckoo search algorithm.  Using Pre Term Birth (PTB) datasets, the empirical analysis is executed and applied using MATLAB. Performance assessment matrix occupied is the precision and our suggested method is compared with the active methods. It is examined that our suggested method has attained improved precision value (85.6 %) when compared to FMM (77.36 %) which illustrates the efficiency of the suggested method from the results
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Keywords


Fuzzy Min–Max (FMM) Neural Network; Decision Making System; Oppositional Cuckoo Search; Amplification Operator; Pattern Classification; Rule Extraction, Performance Evaluation

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