Using ML Embedded System for Predicting of the Medical Symptoms among Patients

Abstract

The authors' previously developed computer programs, the ML (Machine Learning) models, the “Medical Commander” and “Medical Commander Prediction” medical process managers, enable real-time prediction of medical symptoms in clinical practice using two interpolation principles: minimizing the difference between known and interpolated numerical values; and selecting the maximum number of statistical frequencies of observed patients in given intervals as an argument. Based on these principles, an embedded ML system was developed that included a Programmable Logic Controller (PLC) controlled by ML-developed models for the “Medical Commander” and “Medical Commander Prediction” medical process manager. The ML embedded system includes a programmable logic controller (PLC), which is a compact mobile programmable logic controller that includes a built-in console (input-output device), a data input unit for a programmable logic controller, a unit for outputting the results to the monitor screen and the monitor itself allows in real time and in automatic mode to predict the medical symptoms among patients with high precision. The obtained result testifies the possibility of the developed ML embedded system to assess the effectiveness of the preventive treatment.