Reinforcement Learning with Fuzzy Neural Network for Medical Data

Many researchers are currently having difficulty predicting diseases due to the vast number of medical records. Researchers are using data mining techniques like association rules, classification, and clustering to get around these challenges. With the use of prediction, doctors can diagnose a variety of diseases with accuracy. Although the current study classifies the data to forecast specific diseases, overfitting in the training data continues to provide challenges. Recursive Feature Elimination is used to remove unnecessary features from Medical Data. The features are then ranked to lower the computational cost of the suggested approach.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/iceca63461.2024.10800796
Citations
5
Primary Topic
Fuzzy Logic and Control Systems
Type
article
Field-Weighted Citation Impact
1.74
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Reinforcement Learning with Fuzzy Neural Network for Medical Data

G. Malleswari, Rohit Raja, A. Srinivasa Reddy, Anup Kumar
5 citations
Fuzzy Logic and Control Systems
1.74
article

Reinforcement Learning with Fuzzy Neural Network for Medical Data

G. Malleswari, Rohit Raja, A. Srinivasa Reddy, Anup Kumar
article en
5 citations

Abstract

Many researchers are currently having difficulty predicting diseases due to the vast number of medical records. Researchers are using data mining techniques like association rules, classification, and clustering to get around these challenges. With the use of prediction, doctors can diagnose a variety of diseases with accuracy. Although the current study classifies the data to forecast specific diseases, overfitting in the training data continues to provide challenges. Recursive Feature Elimination is used to remove unnecessary features from Medical Data. The features are then ranked to lower the computational cost of the suggested approach.

Openalex Percentile: Top 12%
Fuzzy Logic and Control Systems
1.74
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