Machine learning based dysfunction of thyroid disease

Thyroid disease belongs to the group of diseases which affects people worldwide at an increasing rate in the general population. The classification of thyroid disease between hyperthyroidism and hypothyroidism forms the basis of our research because medical reports have demonstrated severe thyroid imbalances. The disease received its classification through algorithmic methods. Computer learning produced successful outcomes through the combination of Decision Trees (DT) and Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) algorithms which were structured into two separate models. The Decision Tree algorithm reached 98.93 percent accuracy when all 16 input characteristics and the single output variable were included in this model. This proves it has the highest accuracy level among other algorithms. This research shows that the developed machine learning framework shows successful thyroid dysfunction prediction abilities which might help healthcare providers identify and treat thyroid disease cases.