Diagnosis of hypothyroidism using a fuzzy rule-based expert system

Sajadi, Negar Asaad; Borzouei, Shiva; Mahjub, Hossein; Farhadian, Maryam

doi:10.1016/j.cegh.2018.11.007

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…In Asaad Sajadi et al (2019) , hypothyroidism is detected by means of a fuzzy rule-based expert system, which is proved to perform better than a logistic regression model on a real dataset.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The authors in [11], proposed fuzzy logic and LR algorithms for diagnosing hypothyroidism. The thyroid dataset was gathered from Imam Khomeini Clinic and Shahid Beheshti Hospital of Iran.…”

Section: Review Of Related Workmentioning

confidence: 99%

Development of Thyroid Disease Prediction Model in Nigeria

Togor,

Abah,

Kwaghtyo

2023

IJAIS

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Thyroid Diseases are often diagnosed in Nigeria's endocrinology clinics and are reported as the second most common endocrine disease encountered amongst Nigerians, especially women. This led to a vast volume of thyroid data. When applied to machine learning algorithms, the data can greatly benefit endocrinologists, patients, and health organizations. However, diverse machine-learning methods have been in place for thyroid disease prediction leveraging the UCI thyroid data for experimental purposes. Conversely, this study differs by utilizing indigenous thyroid data to predict the thyroid conditions: hypothyroidism, hyperthyroidism and euthyroidism. The dataset was preprocessed using pandas and NumPy libraries. Random Forest classifier and Support Vector Machine classifier were trained using the indigenous dataset. Experimentally, the model was evaluated using Accuracy, Precision, F1-measure, Sensitivity and the Receiver Operating Characteristic (ROC) curve -Area Under the Curve AUC. The classification results show that the Random Forest classifier obtained the best accuracy of 99.30%, while the Support Vector Machine classifier achieved an accuracy of 98.60%. The study achieved the goal of turning the data comprising of thyroid conditions gathered from the Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria into a powerful tool to support endocrinologists, patients, and health organizations.

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“…To diagnose thyroid disorders, a fuzzy rule-based classifier was designed. 97 (35) The current study method (GA-MLP) A comparison of PSO with GAs as training for the MLP method to diagnose thyroid functional disease.…”

Section: Fuzzy Rule-based Expert Systemmentioning

confidence: 99%

Comparison of the Particle Swarm Optimization with the Genetic Algorithms as a Training for Multilayer Perceptron Technique to Diagnose Thyroid Functional Disease

Sharifi

Alizadeh

2020

Shiraz E-Med J

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Background: Thyroid is a gland that controls body key functions. Since thyroid hormones are responsible for controlling metabolism, the thyroid glands are one of the major organs in the body. Disruption of the glands causes thyroid disease, which is one of the most common endocrine diseases worldwide. Early diagnosis of thyroid disease is difficult since early symptoms are easily confused with those of other illnesses. In recent years, the employment of computer techniques to utilize data mining and intelligent algorithms accelerates the early diagnosis of the disease. Objectives: The current study aimed at evaluating the role of the new automatic method according to a multilayer perceptron (MLP) in the diagnosis of thyroid disease. Methods: The study aimed at comparing the particle swarm optimization (PSO) with the genetic algorithms (GA) as training for MLP technique used to diagnose thyroid functional disease. The data were collected for three classes: 150 cases of euthyroidism, 30 cases of hypothyroidism, and 35 cases of hyperthyroidism. MLP was used to elucidate the pattern in the data and species responsible for separating the classes. Furthermore, improved PSO and GAs were used to train the system, and the sensitivity and specificity of the model were studied in terms of accuracy, sensitivity, and specificity. All analyses were performed using MATLAB software. Results: For the proposed model, the simulations results showed that the GA algorithm had a higher performance than the PSO algorithm in the diagnosis of functional thyroid disease, and the means of classification accuracy, sensitivity, and specificity were 95%, 96%, and 96%, respectively. Conclusions: The results of real data indicated that the GA-MLP can be used with high diagnostic accuracy as an effective tool to clinically diagnose thyroid functional disease. The current study was a step towards prototype system development of the classification of knowledge in this area with a much lower computational cost.

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Diagnosis of hypothyroidism using a fuzzy rule-based expert system

Cited by 16 publications

References 23 publications

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Development of Thyroid Disease Prediction Model in Nigeria

Comparison of the Particle Swarm Optimization with the Genetic Algorithms as a Training for Multilayer Perceptron Technique to Diagnose Thyroid Functional Disease

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