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Developing a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners.

Kwang Baek Kim¹, Doo Heon Song², Hyun Jun Park³

¹Department of Artificial Intelligence, Silla University, Busan 46958, Republic of Korea.

Animals : an Open Access Journal From MDPI

|January 8, 2025

Summary

This study introduces a novel neuro-fuzzy learning system for pet dog pre-diagnosis. The multi-layered neuro-fuzzy learner (MNFL) system accurately identifies potential diseases from owner-reported symptoms, enhancing early detection and pet care.

Keywords:

PFCM multi-layered fuzzy inference pet dog disease pre-diagnosis robustness

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Veterinary Medicine
Artificial Intelligence
Machine Learning

Background:

The growing pet market in Korea presents challenges for owners managing pet health.
Non-expert pet owners often struggle with timely diagnosis of their pet's health issues.

Purpose of the Study:

To develop a pre-diagnosis system for pet dogs using neuro-fuzzy learning.
To enable non-expert users to monitor pet health by inputting observed symptoms.
To provide likely disease predictions and coping strategies.

Main Methods:

A disease-symptom database was created with veterinary guidance.
Evaluation of three fuzzy inference algorithms: PFCM-R, FHAL, and the proposed MNFL.
Development of the multi-layered neuro-fuzzy learner (MNFL) for improved robustness.

Main Results:

PFCM-R showed high accuracy with clean data but poor noise tolerance.
FHAL offered better noise tolerance but lower precision.
MNFL achieved 98% accuracy even with noisy inputs, demonstrating superior robustness.

Conclusions:

The MNFL system effectively aids in early detection of pet health issues.
This empowers pet owners for better care and informed veterinary consultations.
The system enhances overall companion animal well-being through improved health monitoring.