Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Main Article Content

Rachappa Jopate
https://orcid.org/0009-0007-4875-5719
Piyush Kumar Pareek
DivyaJyothi M. G
Ariam Saleh Zuwayid Juma Al Hasani

Abstract

People with underactive thyroids frequently endure severe symptoms. Correct classification and machine learning substantially improve thyroid disease diagnosis. This precise classification will impact the timely delivery of care to the patients. Although diagnostic techniques exist, they frequently seek binary categorization, use insufficiently big datasets, and lack confirmation of their conclusions. The focus of current approaches is on model optimisation, whereas feature engineering is neglected. This research presents the Adaptive Elephant Herd Optimisation Algorithm (AEHOA) model for selecting optimal attributes in order to circumvent these limitations. At first, employ a method called the Synthetic Minority Over-sampling Technique (SMOTE) to even out the data. Finally, the parameters of the AEHOA model are fed into a Convolutional Neural Network (CNN) to categorise data and enhance prediction. The accuracy of classification predictions was also increased by tweaking the dataset. Both datasets were put through a categorization process for a more precise comparison of results.

Article Details

How to Cite
1.
Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1786. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10547
Section
Special Issue - (ICCDA) International Conference on Computing and Data Analytics

How to Cite

1.
Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1786. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10547

References

Kumar N, Nandihal P, B MR, Pareek PK, T N, R SS. A Novel Machine Learning-Based Artificial Voice Box. In: 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) 2022; Bangalore, India. IEEE. 2022; p. 1-7. https://doi.org/10.1109/ICATIECE56365.2022.10046967.

Nandihal P, Shetty VS, Guha T, Pareek PK. Glioma Detection using Improved Artificial Neural Network in MRI Images. In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon); 2022; Mysuru, India. IEEE. 2022;p. 1-9. https://doi.org/10.1109/MysuruCon55714.2022.9972712.

Subbalakshmi C, Pareek PK, Narayana MV. A Gravitational Search Algorithm Study on Text Summarization Using NLP. Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science. Cham. 2022; 1673. https://doi.org/10.1007/978-3-031-21385-4_13.

Luna-Guevara JJ, Arenas-Hernandez MMP, Martínez de la Peña C, Silva JL, Luna-Guevara ML. The role of pathogenic E. coli in fresh vegetables: behavior, contamination factors, and preventive measures. Int J Microbiol. 2019; Article ID 2894328. https://doi.org/10.1155/2019/2894328

Ula M, Pratama A, Asbar Y, Fuadi W, Fajri R, Hardi R. A New Model of The Student Attendance Monitoring System Using RFID Technology. J Phys Conf Ser. 2021; 1807(1): 012026. https://doi.org/10.1088/1742-6596/1807/1/012026

Ramteke B, Dongre S. IoT Based Smart Automated Poultry Farm Management System. In: 2022 10th International Conference on Emerging Trends in Engineering and Technology-Signal and Information Processing (ICETET-SIP-22); 2022; pp. 1-4. https://doi.org/10.1109/ICETET-SIP-2254415.2022.9791653

Doaa Mohey El-Din Mohamed Hussein. A survey on sentiment analysis challenges. J King Saud Univ - Eng Sci. 2018; 30(4): 330-338. ISSN 1018-3639. https://doi.org/10.1016/j.jksues.2016.04.002

Hora SK, Poongodan R, Perez de Prado R, Wozniak M, Divakarachari PB. Long short-term memory network-based metaheuristic for effective electric energy consumption prediction. Appl. Sci. 2021; 11(23): 11263. https://doi.org/10.3390/app112311263

Dey S, Ye Q, Sampalli S. A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks. Inf Fusion. 2019; 49: 205-215. https://doi.org/10.1016/j.inffus.2019.01.002

Karthik R, Radhakrishnan M, Rajalakshmi R, Raymann J, Manjunath R, Kwadiki K. Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network. Biomed Eng. Lett. 2021; 11: 3-13. https://doi.org/10.1007/s13534-020-00178-1

Mallik A, Khetarpal A, Kumar S. ConRec: malware classification using convolutional recurrence. J Comput. Virol. Hacking Tech. 2022; 1-17. https://doi.org/ 10.1007/s11416-022-00416-3

Han Y, Liu M, Jing W. Aspect-level drug reviews sentiment analysis based on double BiGRU and knowledge transfer. IEEE Access. 2020; 8: 21314-21325. https://doi.org/10.1109/ACCESS.2020.2969473

Alqahtani A, Alqahtani N, Alsulami AA, Ojo S, Shukla PK, Pandit SV, et al. Classifying electroencephalogram signals using an innovative and effective machine learning method based on chaotic elephant herding optimum. Expert Syst. 2023: e13383. https://doi.org/10.1111/exsy.13383.

Garcia-Perez A, Cegarra-Navarro JG, Sallos MP, Martinez-Caro E, Chinnaswamy A. Resilience in healthcare systems: Cyber security and digital transformation. Technovation. 2023; 121: 102583. ISSN 0166-4972. https://doi.org/10.1016/j.technovation.2022.102583.

Sulaiman R, Schetinin V, Sant P. Review of Machine Learning Approach on Credit Card Fraud Detection. Hum-Cent Intell Syst. 2022; 2: 55–68. https://doi.org/10.1007/s44230-022-00004-0.

Alnaggar, M., Handosa, M., Medhat, T., Z. Rashad, M. Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model. Egypt. J Artif Intell. 2023; 2(1): 1-14. https://doi.org/10.21608/ejai.2023.205554.1008

Tang F, Ding J, Wang L, Ning C. A Novel Distant Domain Transfer Learning Framework for Thyroid Image Classification. Neural Process Lett. 2023; 55(3): 2175-2191. https://doi.org/ 10.1007/s11063-022-10940-4

Sinha BB, Ahsan M, Dhanalakshmi R. Light GBM empowered by whale optimization for thyroid disease detection. Int J Inf Technol. 2023: 1-10. https://doi.org/10.1007/s41870-023-01261-3.

Srivastava R, Kumar P. Optimizing CNN based model for thyroid nodule classification using data augmentation, segmentation and boundary detection techniques. Multimed Tools Appl. 2023: 1-36. https://doi.org/10.1007/s11042-023-15068-8.

Huang SF, Cheng CH. A safe-region imputation method for handling medical data with missing values. Symmetry. 2020; 12(11): 1792. https://doi.org/10.3390/sym12111792.

Amirruddin AD, Muharam FM, Ismail MH, Tan NP, Ismail MF. Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)- sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles. Comput Electron Agric. 2022; 193: 106646. https://doi.org/10.1016/j.compag.2021.106646.

Sen S, Jopate R, Kerur SS, Manjunatha LH, Ahmad A, Jothiprakash G. Nanocomposites for Energy Storage. Materials for Sustainable Energy Storage at the Nanoscale. 1st ed. CRC Press. 2023; p. 331-336. https://doi.org/10.1201/9781003355755.

Falhi A, Luaibi N, Alsaedi A. Hypothyroidism and AMH in Iraqi Patients with Chronic Kidney Disease. Baghdad Sci J. 2021; 18(Suppl. 1): 695-699. https://doi.org/ 10.21123/bsj.2021.18.1(Suppl.).0695.

Alnedawe SM, Aljobouri HK. A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches. Baghdad Sci J. 2023 Aug. 1 [cited 2024 Jan. 14]; 20(4): 1402. https://orcid.org/0000-0003-1361-2607.

Bao Y, Yang S. Two Novel SMOTE Methods for Solving Imbalanced Classification Problems. IEEE Access. 2023; 11: 5816-5823. https://doi.org/10.1109/ACCESS.2023.3236794.

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