Optimized machine learning algorithm for thyroid tumour type classification: A hybrid approach Random Forest, and intelligent optimization algorithms

Article type: Research Article

Authors: Sheik Faritha Begum, S.^a | Suresh Anand, M.^b | Pramila, P.V.^c | Indra, J.^d | Samson Isaac, J.^e | Alagappan, Chockalingam^f | Gopala Gupta, Amara S.A.L.G.^g | Srivastava, Suraj^h | Vidhya, R.G.^{i; *}

Affiliations: [a] Department of Computer Science & Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India | [b] Department of Computing Technologies, School of Computing, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India | [c] Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India | [d] Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India | [e] Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India | [f] Department of Electrical and Electronics Engineering, M. Kumarasamy Collegeof Engineering, Karur, Tamil Nadu, India | [g] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [h] Department of Computer Science and Engineering, Punjab Engineering College, Chandigarh, India | [i] Postdoctoral Researcher, Ulm University, Germany

Correspondence: [*] Corresponding author. R.G. Vidhya, Postdoctoral Researcher, Ulm University, Germany. E-mail: vidhya50.ece@gmail.com.

Abstract: Thyroid tumours are a common form of cancer, and accurate classification of their type is crucial for effective treatment planning. This research presents a hybrid approach for the classification of thyroid tumours based on their type. The proposed approach combines the use of advanced machine learning techniques with a comprehensive database of thyroid tumour samples. The database includes various features such as tumour size, shape, and texture, as well as patient-specific information. The hybrid approach aims to optimize the classification process by leveraging the diverse set of features and utilizing the power of machine learning algorithms. By harnessing the power of machine learning algorithms, this approach has the potential to revolutionize the field of thyroid tumour classification and significantly improve patient outcomes. The optimization strategy is Particle Swarm Optimization, refining the classification performance and ensuring optimal accuracy in identifying and categorizing four types of thyroid tumours. The utilization of advanced diagnostic tools and state-of-the-art Random forest classifier techniques in this approach marks a significant advancement in the field of thyroid tumour classification. Through the augmentation of the dataset and the pre-processing techniques employed, the hybrid classification system demonstrates enhanced accuracy and reliability in distinguishing between different types of thyroid tumours. This innovative approach not only provides a more comprehensive understanding of thyroid tumours but also paves the way for personalized and effective treatment strategies, ultimately improving patient care and outcomes.

Keywords: Machine learning, thyroid tumours, Particle Swarm Optimization, Random Forest classifier, innovative approach

DOI: 10.3233/JIFS-239804

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024