Open Access Subscription or Fee Access
Study on Diabetes Prediction Techniques using Software Applications
The data even after cleaning are not ready for mining as we need to transform them into forms appropriate for mining. The techniques used to accomplish this are smoothing, aggregation, normalization etc. Data Mining: Now we are ready to apply data mining techniques on the data to discover the interesting patterns. Techniques like clustering and association analysis are among the many different techniques used for data mining. Pattern Evaluation and Knowledge. This paper concentrates on the overall literature survey related to various data mining techniques: decision tree, random forest, naïve Bayes, k-nearest neighbors, naïve bayes and deep learning for predicting diabetes and discussed the its limitations.
Diabetes Mellitus, Data Mining, Prediction, Decision Tree, Classification,
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.