NOVEL HYBRID LSTBAM - BIDIRECTIONAL ASSOCIATIVE MEMORY DEEP LEARNING BASED THYROID DISEASE PREDICTION

Authors

  • D. PRIYADHARSINI, Dr. S. SASIKALA

DOI:

https://doi.org/10.47750/pnr.2022.13.S10.138

Abstract

Deep learning techniques play a vital role in the disease prediction. This research focus on the hypothyroid disease prediction using novel hybrid LSTBAM – Long Short Term Bidirectional Associative Memory based deep learning artificial neural networks prediction method. The dataset deployed has the parameters which portray the disease traits and it has 29 attributes. Results are disclosed which shows the promising enhancement.

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Published

2022-12-31 — Updated on 2022-12-31

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Section

Articles

How to Cite

NOVEL HYBRID LSTBAM - BIDIRECTIONAL ASSOCIATIVE MEMORY DEEP LEARNING BASED THYROID DISEASE PREDICTION . (2022). Journal of Pharmaceutical Negative Results, 1179-1185. https://doi.org/10.47750/pnr.2022.13.S10.138