Development a Model for Drug Interaction Prediction Based on Patient State

Abstract

Drug interactions prediction is one of the health critical issues in drug producing and use. Proposing computational model for classifying and predicting interactions of drugs with high precision is a difficult problem. Medicines are classified into two classes: overlapping, non-overlapping. It was suggested an expert system for classifying and predicting interactions of drugs using various information about drugs, interference reasons and common factors between patients and active substance that causes interference, such as: effective dose of the drug, maximum dose, times of use per day and age of patients considering that only adult category selected. The proposed model can classify and predict interactions of drugs through patient's state taking into consideration that when changing one of mentioned factors, the effect of drugs will be changed and it may lead to appear new symptoms on the patients. There is a desktop application related with the mentioned model, which helps users to know drugs and drugs families and its interactions. Proposed model will be implemented in Python using following classifiers: Logistic Regression (LR), Support Vector Machine (SVM) and Neural Network (NN), which divided data according to their similarity related to the factors of occurrence of drug interference. As these techniques showed good results, NN technology is considered one of the best techniques in giving results where MLPClassifier achieved superior performance with 97.12%.