Skip to content
Research Article Open access CC BY 4.0

Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

Ibrahim Goni, Christopher U. Ngene, Manga I., Auwal Nata’ala, Sunday J. Calvin

Asian Journal of Research in Computer Science · pp. 1–9 · Published 25 Oct 2018

10.9734/ajrcos/2018/v2i124763

Abstract

Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2.   

Adaptive neuro-fuzzy tuberculosis Gaussian membership function hybrid training algorithm epoch

Cited by 11

Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

N. Khan, Chinyere Nwafor Okoli, V. Ekpin · Expert systems with applications · 2023

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Manisha Singh, G. Pujar, Sethu Arun Kumar · Electronics · 2022

Diagnosis of Pulmonary Tuberculosis through Intelligent Techniques: A Review

Abdul Karim Siddiqui, V. Garg · International Conference on Conceptual Structures · 2021

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

Abdul Karim Siddiqui, V. Garg · SN Computer Science · 2021

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Dr. Adetokunbo MacGregor John-Otumu, Godswill U. Ogba, Obi Nwokonkwo · Journal of Advances in Science and Engineering · 2020

Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review

Haneet Kour, J. Manhas, Vinod Sharma · Artificial Intelligence Review · 2020

Image Processing Approach to Determine the Severity Level of Tuberculosis

N. Auwal, Ibrahim Goni, Danladi Ali · Current Journal of Applied Science and Technology · 2019

A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms

S. E. Nnebe, Nora Augusta Ozemoya Okoh, Dr. Adetokunbo MacGregor John-Otumu · American Journal of Artificial Intelligence · 2019

A Classification Model for the Risk of Tuberculosis in Nigeria Using Data Mining Approach

N. C. Egejuru, B. O. Fajimi, T. Omodunbi · 2021

Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method

Sıraj Sebhatu, Pooja, Parmd Nand · Smart Innovation, Systems and Technologies · 2022

Related research

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

11

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.