Abstract
At present days, world is facing several issues like irregular distribution of medicinal resources, new chronic diseases, and the raising operating cost. The way of combining recent technologies into the medical system will helps to significantly resolve the problems. This study introduces a big health application system based on optimal artificial neural network (OANN) for heart disease diagnosis, which is considered as a deadliest disease in all over the globe. The proposed OANN includes a set of two main processes namely, distance based misclassified instance removal (DBMIR) and teaching and learning based optimization (TLBO) algorithm for ANN, called (TLBO-ANN). The proposed model is developed using a Big Data framework like Apache Spark. The presented OANN model operates on two phases, namely offline prediction and online prediction. During the offline prediction stage, the benchmark heart disease dataset will be used to train a model and performs testing. Similarly, at the online prediction stage, the real time data will be streamed into Apache Spark model and the filtered data will be diagnosed by the use of trained model to obtain the prediction results. The performance of the presented OANN model has been tested using a benchmark heart disease dataset from UCI repository. A comprehensive experimental result analysis clearly verified the better outcome of the OANN model over the compared methods. The proposed method is found to be an effective tool to analyze big data based heart disease prediction model to satisfy the need of increasing number of heart patients.
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06 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04077-4
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This article has been retracted. Please seethe retraction notice for more detail: https://doi.org/10.1007/s12652-022-04077-4
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Thanga Selvi, R., Muthulakshmi, I. RETRACTED ARTICLE: An optimal artificial neural network based big data application for heart disease diagnosis and classification model. J Ambient Intell Human Comput 12, 6129–6139 (2021). https://doi.org/10.1007/s12652-020-02181-x
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DOI: https://doi.org/10.1007/s12652-020-02181-x