Development of a sensory-neural network for medical diagnosing

TL;DR

This paper presents a sensory-neural network model that diagnoses diseases based on patient questionnaire responses, using sensor-like signals and neuron excitation levels to identify the most probable disease with an estimated reliability.

Contribution

It introduces a novel sensory-neural network architecture that models disease diagnosis through sensor-generated signals and neuron excitation analysis.

Findings

The network can identify diseases based on symptom signals.

Diagnosis reliability is estimated by neuron excitation ratios.

The model demonstrates effective disease classification.

Abstract

Performance of a sensory-neural network developed for diagnosing of diseases is described. Information about patient's condition is provided by answers to the questionnaire. Questions correspond to sensors generating signals when patients acknowledge symptoms. These signals excite neurons in which characteristics of the diseases are represented by synaptic weights associated with indicators of symptoms. The disease corresponding to the most excited neuron is proposed as the result of diagnosing. Its reliability is estimated by the likelihood defined by the ratio of excitation of the most excited neuron and the complete neural network.