We have developed a heart disease prediction model using distributed storage,computing technology,and the naive Bayes algorithm.Firstly,we built a Hadoop fully distributed platform and combined it with the Python language and MapReduce programming framework to construct the naive Bayes classifier.Additionally,we parallelized the algorithm using MapReduce to im-prove analysis efficiency.Using the 2020 US CDC dataset as the heart disease dataset,the accuracy of the algorithm reached 88.52%in the test set.Furthermore,our solution has been validated to be able to successfully predict whether an individual is suf-fering from heart disease in practical applications.This solution has high accuracy and advantages such as high scalability,distrib-uted storage and computing,and fault tolerance,forming a reliable,efficient,and low-cost solution.
维普
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