Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset

Olatunji, Sunday O.; Alotaibi, Sarah; Almutairi, Ebtisam; Alrabae, Zainab; Almajid, Yasmeen; Altabee, Rahaf; Altassan, Mona; Ahmed, Mohammed Imran Basheer; Farooqui, Mehwash; Alhiyafi, Jamal

doi:10.1016/j.compbiomed.2021.104267

Cited by 34 publications

(19 citation statements)

References 16 publications

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“…Several other machine learning, deep learning and AI based techniques have been applied for disease predictions in the literature and it is among the hottest areas of research in healthcare and several other disciplines [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally. To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study. In the preprocessed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds. The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks. We used a dataset from PhysioNet website that contains adult EEG signals. Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results. Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.

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Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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“…Equations 6 , 7 ) show how precision and recall are computed, where precision calculates the amount of true positive prediction belonging to the positive class, and recall calculates true positive prediction belonging to all positive samples. Equation (8) shows how the ƒ˗Measure is calculated, which estimates the performance of each class [ 31 ].

…”

Section: Empirical Studiesmentioning

confidence: 99%

Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach

Albagmi

Alansari

Shawan

et al. 2022

Informatics in Medicine Unlocked

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“…In the research the best results came from using the RF technique with an accuracy of 90.94%. [24] In their study Santillan et al use the FTIR spectroscopy with the help of neural networks to obtain their model. The data consisted of 76 malignant and 88 benign tumors.…”

Section: Thyroid Cytologymentioning

confidence: 99%

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

et al. 2022

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Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions. INDEX TERMSCancer diagnosis, Image processing, Neural networks I.

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Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset

Cited by 34 publications

References 16 publications

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ensemble Machine Learning Based Identification of Adult Epilepsy

Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

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