# Method for Detecting Pathology of Internal Organs Using Bioelectrography

## TL;DR

The paper introduces a new method using bioelectrography and machine learning to detect internal organ diseases, improving detection accuracy and reducing the workload on medical staff.

## Contribution

A novel method for detecting internal organ pathology using bioelectrography combined with optimized machine learning models is developed.

## Key findings

- HyperTab, logistic regression, and xgboost achieved 60–70% f1-score performance in detecting organ pathology.
- The method enables detection of combined pathology and improves screening efficiency.
- A software package was developed to implement the method using bioelectrography data.

## Abstract

This article considers the possibility of using the bioelectrography method to identify the pathology of internal organs. It is shown that with the currently existing methods, there is no possibility of the automatic detection of diseases or abnormalities in the functioning of a particular organ, or of the definition of combined pathology. It has been revealed that the use of various classifiers makes it possible to expand the field of pathology and choose the most optimal method for determining a particular disease. Based on this, a method for detecting the pathology of internal organs is developed, as well as a software package that allows the detection of diseases of the internal organs based on the bioelectrography results. Machine-learning models such as logistic regression, decision tree, random forest, xgboost, KNN, SVM and HyperTab are used for this purpose. HyperTab, logistic regression and xgboost turn out to be the best among them for this task, achieving a performance according to the f1-score metric in the order of 60–70%. The use of the developed method will, in practice, allow us to switch to combining various machine-learning models for the identification of certain diseases, as well as for the identification of combined pathology, which will help solve the problem of detecting pathology during screening studies and lead to a reduction in the burden on the staff of medical institutions.