A New Heuristic Hybrid Model for Cancer-Based P-Glycoprotein Drug Classification: Automated Prediction

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As the population improves in real-time applications, drug identification becomes an increasingly difficult task. Various biotechnological applications based on in vitro fertilization can benefit from better drug identification. For many nations, agriculture is the backbone of cancer medication development; increasing crop yields is the key to better drug identification in agricultural contexts. Computer vision applications suffer as a result of the efficient degradation of production quality and quantity caused by p-glycoprotein drugs used in cancer treatment. The identification of cancer-based p-glycoprotein drugs is an aggressive concept in the present day, due to the various symptoms present in cancer. Various authors use soft computing techniques and machine learning approaches to predict these drugs. For the purpose of automatically identifying based p-glycoprotein drugs in cancers, this paper proposes a Hybrid Novel Heuristic Model (HNHM) that combines Self-Organizing Maps (SOMs) with Convolution neural networks (CNNs). The primary challenge in identifying cancer drugs based on p-glycoprotein is feature extraction; hence, dimensionality reduction is crucial in filtering out noise from the cancer sub-cancer drug dataset and separating the subset of the cancer drug dataset affected by the p-glycoprotein drug using support vector machines (SOM). It is a promising approach to automatically identify based p-glycoprotein drugs in cancers by exploring protein features in cancer and predicting which patches are affected by virus-bacteria. CNN is used to match cancer sub-cancer drug data sets. In this study, we use sub-Cancer drug data sets to develop a hybrid heuristic approach to identify based p-glycoprotein drugs found in cancers. Clinical trials of the suggested hybrid model were conducted using sub-cancer drug data sets derived from peach cancers and the publicly available Cancer's drug datasets housed in the UCI repository. With less training required to explore based on p-glycoprotein drugs from sub-Cancer drug datasets, the suggested hybrid model achieves an accuracy of nearly 99.93% when tested on different testing datasets for sub cancer drugs. When compared to other state-of-the-art methods, this one significantly reduces running time while providing efficient performance in a number of p-glycoprotein drug prediction metrics, including recall, sensitivity, and specificity