Drug Side-effects Prediction using Hierarchical Fuzzy Deep Learning for Diagnosing Specific Disease

Drug Side-effects Prediction using Hierarchical Fuzzy Deep Learning for Diagnosing Specific Disease
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Nithya B, Anitha G
DOI : 10.14445/22315381/IJETT-V70I8P214

How to Cite?

Nithya B, Anitha G, "Drug Side-effects Prediction using Hierarchical Fuzzy Deep Learning for Diagnosing Specific Disease," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 140-148, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P214

Abstract
In drug discovery, the foremost challenging task is predicting drug-disease correlation using drugs' various indications and side effects on specific diseases for proper diagnosis. To combat this issue, Wasserstein Auto-Encoder with Convolutional Neural Network (WAE-CNN) model was developed, which uses the side effects constraints along with the drugs and patient attributes from the large-scale databases to predict drugs for specific diseases. But, the correlation variability between several drug-disease side effect categories is quite unfair. Few categories are more complex to predict than others. Therefore, this article presents a Hierarchical Fuzzy Deep CNN (HFDCNN)model to predict and recommend drugs for particular diseases considering side effects. First, the database is created by collecting data about patients, diseases, drugs and their side effects. Then, such data are fed to the HFDCNN for prediction. In the HFDCNN model, FDCNN is embedded into the attribute hierarchy. It segregates simple classes using a coarse classifier, whereas fine classifiers differentiate complex classes. In the learning phase, an element-wise pre-learning is supported by global fine-tuning with a multinomial logistic loss normalized by a coarse coherence factor. Also, conditional executions of fine classifiers and layer variable reduction make this HFDCNN more robust for many disease data associated with the drugs and their side effects. Finally, the test results exhibit that the HFDCNN model achieves 95.3%, 97.1% and 98.55% of accuracies in predicting the drugs for Chronic Kidney Disease (CKD), diabetes and heart diseases, correspondingly compared to the classical models.

Keywords
Drug-disease correlation, Side effects, WAE-CNN, Attribute hierarchy, Fuzzy DCNN, Multinomial logistic loss, Conditional execution.

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