Healthcare Biclustering of Predictive Gene Expression Using LSTM Based Support Vector Machine
, Joy Elvine Martis, Sunil Kumar M, Sudha S, Iyappan P, Natarajan CThe major goal of this work is to establish prediction patterns that can influence better diagnosis and treatment strategies using unidentified interactions between genes.
Driven by the rapid advances in genomics, knowledge of the factors causing disease depends more on deciphering the deep linkages existent in the data of gene expression. Common approaches typically fail to grasp temporal links when dealing with always-changing living biological systems. This work overcomes this restriction by leveraging the sequential learning abilities of LSTM together with the improved pattern recognition capacity of SVM.
Our method uses a hybrid model combining LSTM and SVM to forecast gene expression. Working together, the LSTM and SVM components find relevant features in the gene expression data, clarifying trends in the data. Furthermore, the LSTM component oversees data temporal dependencies. Regarding accuracy and interpretability, this extra method helps to improve prediction models used in the healthcare industry.
There are many ways to get a key insight from data on gene expression. The LSTM and SVM for biclustering gene expression data offer much for healthcare informatics.
The proposed LSTM-based SVM is used to evaluate numerous current methods of evaluating performance metrics. Using these opens several opportunities for the development of customized medicine and the customization of therapies in line with personal genetic profiles.
Examining the LSTM-SVM hybrid model that has been proposed using a variety of healthcare-related datasets
This work can be enhanced using several deep-learning algorithms to achieve better accuracy and performance.
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