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- W4232184414 abstract "In that paper, we’ve an inclination to project as checking the whole patient ill health victimization Naive Bayes classification and J48 decision tree. As a result of the information, enormous process comes from mu ltiple, heterogeneous, autonomous sources with sophisticated an d evolving relationships and continues to grow. So in that, we’ll take results of what proportion share patients get ill health as a positive knowledge and negative knowledge. Huge info is difficult to work with victimization most database management systems and desktop statistics and internal representation packages. The projected shows a huge process model, from the data mining perspective. Victimization classifiers, we’ve an inclination to unit method congenital disease share and values unit showing as a confusion matrix. We’ve an inclination to projected a replacement classification theme which could effectively improve the classification performance inside the situation that employment dataset is out there. During this dataset, we have nearly 1000 patient details. We’ll get all that details from there. Then we have a tendency to unit attending to sensible and unhealthy values square measure victimization naive Bayes classifier and J48 tree" @default.
- W4232184414 created "2022-05-12" @default.
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- W4232184414 date "2019-07-30" @default.
- W4232184414 modified "2023-09-26" @default.
- W4232184414 title "Big Data Analysis for Diabetes Recognition using Classification Algorithms" @default.
- W4232184414 doi "https://doi.org/10.35940/ijrte.a1333.078219" @default.
- W4232184414 hasPublicationYear "2019" @default.
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