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Improved bio-inspired with machine learning computing approach for thyroid prediction.

Divya Kesavulu1, Kannadasan R2

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Summary

Machine learning models, enhanced with particle snake swarm optimization (PSSO), significantly improve thyroid illness prediction accuracy. The PSSO-Random Forest model achieved 98.7% accuracy, outperforming deep learning methods.

Keywords:
CNN-LSTMDTDeep learningFeature selectionKNNMachine learningOptimisation techniquesParticle snake swarm optimizationRFSVMThyroid

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Artificial Intelligence in Healthcare

Background:

  • Thyroid disorders, including hypothyroidism and hyperthyroidism, are prevalent global health issues with significant metabolic and well-being impacts, particularly affecting women in Asia, Latin America, and Africa.
  • Accurate and timely diagnosis of thyroid diseases is crucial for effective patient management and preventing associated health complications.

Purpose of the Study:

  • To investigate the efficacy of various machine learning (ML) and deep learning (DL) models for enhancing the precision of thyroid illness prediction.
  • To optimize ML model performance using advanced techniques like particle snake swarm optimization (PSSO).

Main Methods:

  • Evaluation of multiple machine learning algorithms including Random Forest (RF), Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
  • Application of particle snake swarm optimization (PSSO) to enhance the predictive capabilities of the selected ML models.
  • Performance assessment using key metrics: accuracy, recall, precision, F1-score, and specificity.

Main Results:

  • The Random Forest model optimized with PSSO (PSSO-RF) demonstrated superior predictive performance, achieving 98.7% accuracy.
  • PSSO-RF significantly outperformed a CNN-LSTM deep learning baseline, with an accuracy improvement of 2.98% (98.7% vs. 95.72%).
  • The optimized model achieved high scores across all evaluated metrics: 98.47% F1-score, 98.51% precision, 98.7% recall, and 98% specificity.

Conclusions:

  • Bio-inspired optimization techniques, such as PSSO, can substantially enhance the performance of conventional machine learning models for disease prediction.
  • The PSSO-RF model represents a highly effective computational approach for accurate thyroid illness detection, surpassing current state-of-the-art methods.
  • This research underscores the potential of integrating advanced computational innovations into healthcare for improved diagnostic accuracy and patient outcomes.