Research on random forest drug classification prediction model based on KMeans-SMOTE

Scientific medication is of great significance to improve the therapeutic effect of diseases. The etiology of chronic diseases is complex, different patients have different responses to drugs. Analyzing the examination results and medication information of previous patients can make the treatment plan follow a regular basis. First, the existing medical data is used to visualize the examination results of previous patients and the drugs used, to find the corresponding relationship between the two, and to build new attributes based on the attributes of the original data to improve the accuracy. Then, three oversampling algorithms are used to balance the imbalanced properties of the dataset. Finally, five kinds of classifiers were used to build a drug prediction model and conduct comparative experiments. The results showed that the random forest model based on the KMeans-SMOTE showed good results in various evaluations, and the drug prediction accuracy rate reached 95.05%. The model can better predict the drugs needed by patients, and its performance is better than other established models.