Graph mining approaches for drug selection using fuzzy multilayer neural perception with marginal subset clustering features

The biomedical industry uses graph mining to store a variety of data. It features a feature that makes a lot of info accessible. However, the majority of individuals are mainly concerned with learning about illnesses and medications. Creating drug models and novel chemical molecules in the medical industry is called relational medicine. Patients may have adverse reactions to the medication due to the dissimilarity of the compound's molecules. It suggests that complex molecular characteristics don't affect the dataset's classification and are independent of one another. We aim to increase the effectiveness of graph mining-based drug selection by analyzing the success rate of drug selection using Fuzzy Multilayer Neural Perception (FMNP). Marginal Subset Clustering Features (MSCF), the input used to generate the chosen classes, are processed utilizing these features. The system first preprocessed all patient features and suggested medication molecule compounds to create a consolidated dataset. Distance vector features linked to edge weights are used in feature selection to create relationship patterns. The features are chosen based on the estimated Relational Drug Combination Weight (RDCW). Additionally, the implementation updates the logic rules and gives the neural classifier predictions for feature weights. Using a neural classifier and iterative logic rules, FMNP forecasts training outcomes. The classifier continuously predicts and suggests chemical molecules to lower the possibility of adversative effects.