Drugs Classifier System Based on Machine Learning Algorithms

Abstract

Background/objectives: Nowadays, there are thousands of approved drugs that can be used for treating people who have medical problems. Therefore, drug warnings and precautions are denoted to recognize a discrete set of adverse effects and other implied protection uncertainties that are useful for patient control. Methods/analysis/findings: In this study, the intended framework is divided into two principal stages: data retrieval and data processing. Firstly, in the data collection stage, drug reports, drug interactions, malfunctions, number of deaths, and other factors had been obtained from various references, including RxNorm and Drug Bank using web service. Secondly, in the data processing phase, different data mining algorithms used to classify drugs into suitable drugs and non-suitable drugs. Application/improvements: According to the experimental results, we found that the decision tree has more accuracy (97.9%) than other state-of-art methods.

Handwritten Urdu character recognition via images using different machine learning and deep learning techniques

“…It is a supervised learning algorithm. An SVM algorithm creates a model which breaks data into categories and assigns newly created categories to each set of data which makes SVM a non-probabilistic binary linear classifier (14) .…”