An Ensemble Approach for Classification of Thyroid Using Machine Learning

Dharamkar, Bhavna; Saurabh, Praneet; Prasad, Ram; Mewada, Pradeep

doi:10.1007/978-981-15-2414-1_2

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…The method was implemented in the real environment on a real-time dataset and achieved 99.2% accuracy. An ensemble [22] approach of thyroid disease classification was defined by combining C4.5 and Random Forest techniques. The proposed method achieved 96% accuracy on average.…”

Section: Related Workmentioning

confidence: 99%

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Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Juneja

2022

Wireless Pers Commun

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Thyroid disorder affects the regulation of various metabolic processes throughout the human body. The structural and functional disorders can affect the body as well as the brain. The computer-aided diagnosis system can identify the kind of Thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. In this paper, a fuzzy adaptive feature filtration, expansion, and again filtration based model is presented for generating the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multimeasure evaluation. At the first level, the composite-fuzzy measures are combined with expert's recommendations for identifying the ranked and relevant features. At the second level, the statistical computation based distance measure is applied for expanding the featureset. The fuzzification is applied to expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the ELM classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the NaiveBayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and RBF Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy based parameters. The proposed framework clams the significant gain in accuracy, relevancy, and reduction in the error rate.

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Section: Related Workmentioning

confidence: 99%

“…MAE is the average magnitude of the errors in a set of predictions. Equation(22) represents the computation of MAE.…”

mentioning

confidence: 99%

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Juneja

2022

Wireless Pers Commun

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“…Algorithms Classification Accuracy Sparano et al (6) Logistic Regression 97.8% Dharamkar et al (7) Fusing C4.5 and random forest 97%…”

Section: Authorsmentioning

confidence: 99%

A Novel Classification Methodology for Thyroid Cancer using C4.5 with Firefly Optimization Algorithm (CFOA)

Vanitha¹,

Perumal²

2022

IJST

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Objectives:The main objective of this research is to detect thyroid cancer in its early stages and to improve the accuracy using a novel method C4.5 with Firefly Optimization Algorithm (CFOA). This research also focuses on developing an effective machine learning-based accurate prediction model. Methods: The images in this investigation are associated to thyroid disorder with 4672 samples of people including both females and males having hypothyroidism and hyperthyroidism, as well as healthy people without thyroid disorder. The data was gathered for one year with the primary goal of classifying thyroid disease using machine learning (ML) algorithms. These data comprise of Gender, Age, Thyroid hormone (T4), Triiodothyronine (T3), Thyroid stimulating hormone (TSH), etc. The performance of this proposed method was measured using parameters such as accuracy, precision, F1-score and recall. Findings: The performance of this proposed method was assessed with the state-ofthe-art existing methods like Naive Bayes (NB) algorithm, K-Nearest Neighbor (KNN) Algorithm and Adaboost. The proposed algorithm showed maximum precision of 0.9935, recall of 0.9971, F1-score of 0.9951 and accuracy of 0.9981 respectively when compared with the existing algorithms. Novelty: A novel algorithm C4.5 with Firefly Optimization Algorithm was proposed in this paper to speed-up and to increase the effectiveness of the machine learning algorithm. Moreover, this study focuses on developing an accurate prediction model by comparing classification algorithms based on accuracy and confusion matrices and then identifying the most effective classifier based on performance.

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“…MAE is the average magnitude of the errors in a set of predictions. Equation (22) represents the computation of MAE.…”

Section: Maementioning

confidence: 99%

Expanded and Filtered Features based ELM Model for Thyroid Disease Classification

Juneja¹

2021

Preprint

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Thyroid disorder affects the regulation of various metabolic processes throughout the human body. The structural and functional disorders can affect the body as well as the brain. The computer-aided diagnosis system can identify the kind of Thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. In this paper, a fuzzy adaptive feature filtration, expansion, and again filtration based model is presented for generating the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multi-measure evaluation. At the first level, the composite-fuzzy measures are combined with expert’s recommendations for identifying the ranked and relevant features. At the second level, the statistical computation based distance measure is applied for expanding the featureset. The fuzzification is applied to expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the ELM classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the NaiveBayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and RBF Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy based parameters. The proposed framework clams the significant gain in accuracy, relevancy, and reduction in the error rate.

show abstract

An Ensemble Approach for Classification of Thyroid Using Machine Learning

Cited by 10 publications

References 10 publications

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

A Novel Classification Methodology for Thyroid Cancer using C4.5 with Firefly Optimization Algorithm (CFOA)

Expanded and Filtered Features based ELM Model for Thyroid Disease Classification

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