Data Mining Classification Algorithms For Kidney Disease Prediction: Data Mining - Punam R. Patil,Bhushan V. Patil
-20% with code BOOKS
Shipping in 12-18 days
30-day return policy
Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering.In the health care industry, the data mining is predominantly used for disease prediction.The main objective is to predict kidney diseases using classification algorithms such as Naive Bayes and Support Vector Machine. This mainly ... Full description
Description
Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering.In the health care industry, the data mining is predominantly used for disease prediction.The main objective is to predict kidney diseases using classification algorithms such as Naive Bayes and Support Vector Machine. This mainly focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors.This algorithm takes symptoms as input and predicts the disease based on patients data.
Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering.In the health care industry, the data mining is predominantly used for disease prediction.The main objective is to predict kidney diseases using classification algorithms such as Naive Bayes and Support Vector Machine. This mainly focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors.This algorithm takes symptoms as input and predicts the disease based on patients data.
More Information
| Author | Punam R. Patil, Bhushan V. Patil |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2021 |
| Cover type | Softcover |
| EAN | 9786203863093 |