Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique

Noor Afshan; Zohaib Mushtaq; Faten S. Alamri; Muhammad Farrukh Qureshi; Nabeel Ahmed Khan; Imran Siddique; Noor Afshan; Zohaib Mushtaq; Faten S. Alamri; Muhammad Farrukh Qureshi; Nabeel Ahmed Khan; Imran Siddique

doi:10.3934/math.20231238

AIMS Mathematics

2023, Volume 8, Issue 10: 24274-24309. doi: 10.3934/math.20231238

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Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique

1.
Department of Software Engineering, Faculty of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan
2.
Department of Electrical Engineering, CET, University of Sargodha, Sargodha 40100, Pakistan
3.
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4.
Department of Electrical Engineering, Riphah International University, Islamabad 44000, Pakistan
5.
Department of Mathematics, University of Management and Technology, Lahore 54770, Pakistan

Received: 27 April 2023 Revised: 08 July 2023 Accepted: 01 August 2023 Published: 14 August 2023
MSC : 62J02, 62J99

There are millions of people suffering from thyroid disease all over the world. For thyroid cancer to be effectively treated and managed, a correct diagnosis is necessary. In this article, we suggest an innovative approach for diagnosing thyroid disease that combines an adaptive synthetic sampling method with weighted average voting (WAV) ensemble of two distinct super learners (SLs). Resampling techniques are used in the suggested methodology to correct the class imbalance in the datasets and a group of two SLs made up of various base estimators and meta-estimators is used to increase the accuracy of thyroid cancer identification. To assess the effectiveness of our suggested methodology, we used two publicly accessible datasets: the KEEL thyroid illness (Dataset1) and the hypothyroid dataset (Dataset2) from the UCI repository. The findings of using the adaptive synthetic (ADASYN) sampling technique in both datasets revealed considerable gains in accuracy, precision, recall and F1-score. The WAV ensemble of the two distinct SLs that were deployed exhibited improved performance when compared to prior existing studies on identical datasets and produced higher prediction accuracy than any individual model alone. The suggested methodology has the potential to increase the accuracy of thyroid cancer categorization and could assist with patient diagnosis and treatment. The WAV ensemble strategy computational complexity and the ideal choice of base estimators in SLs continue to be constraints of this study that call for further investigation.
- thyroid cancer,
- cancer detection,
- hypothyroid,
- machine learning,
- super learners
Citation: Noor Afshan, Zohaib Mushtaq, Faten S. Alamri, Muhammad Farrukh Qureshi, Nabeel Ahmed Khan, Imran Siddique. Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique[J]. AIMS Mathematics, 2023, 8(10): 24274-24309. doi: 10.3934/math.20231238

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Abstract

There are millions of people suffering from thyroid disease all over the world. For thyroid cancer to be effectively treated and managed, a correct diagnosis is necessary. In this article, we suggest an innovative approach for diagnosing thyroid disease that combines an adaptive synthetic sampling method with weighted average voting (WAV) ensemble of two distinct super learners (SLs). Resampling techniques are used in the suggested methodology to correct the class imbalance in the datasets and a group of two SLs made up of various base estimators and meta-estimators is used to increase the accuracy of thyroid cancer identification. To assess the effectiveness of our suggested methodology, we used two publicly accessible datasets: the KEEL thyroid illness (Dataset1) and the hypothyroid dataset (Dataset2) from the UCI repository. The findings of using the adaptive synthetic (ADASYN) sampling technique in both datasets revealed considerable gains in accuracy, precision, recall and F1-score. The WAV ensemble of the two distinct SLs that were deployed exhibited improved performance when compared to prior existing studies on identical datasets and produced higher prediction accuracy than any individual model alone. The suggested methodology has the potential to increase the accuracy of thyroid cancer categorization and could assist with patient diagnosis and treatment. The WAV ensemble strategy computational complexity and the ideal choice of base estimators in SLs continue to be constraints of this study that call for further investigation.

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