Automated classification of thyroid disease using deep learning with neuroevolution model training

Background:

Thyroid disease is a common endocrine disorder; its timely and accurate diagnosis is important. Using clinical and laboratory data input, we developed an artificial neural network (ANN) for thyroid disease classification, incorporating an evolutionary algorithm for network optimization.

Methods:

The proposed model combined ANN with a genetic algorithm (GA), which iteratively modified the weights and biases of the ANN architecture. The weights, encoded as genes in a chromosome and represented as one-dimensional vectors, were updated in each iteration. Binary cross-entropy loss was used as the fitness function to calculate the suitability of solutions in the genetic algorithm. The model was trained and tested on an open-access Hypothyroid disease dataset comprising multiparametric variables of 3772 samples (291 thyroid patients and 3481 controls).

Results:

Our model attends $\text{\%}99.14$ accuracy for binary classification (thyroid disease vs. normal), outperforming published models.

Conclusion:

Incorporating GA optimization into ANN enabled the model to explore diverse solutions and escape local optima more effectively, leading to better performance and generalizability. The excellent results support the feasibility of implementing the proposed model for thyroid disease screening in the clinical setting.