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

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.