Identification and Selection of Random Forest Algorithm for Predicting Hypothyroid

This study uses the Random Forest method to detect hypothyroidism, and it is presented in this publication along with a comparison of its performance with two other machine learning algorithms, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), which were previously applied in related studies. The study was conducted by comparing the performance of the three algorithms on the same dataset, and evaluating the results using various metrics. The outcomes show that when applied for hypothyroidism prediction, the Random Forest method surpasses KNN and SVM in terms of prediction accuracy and efficiency. The study offers insights on the advantages and limitations of using different machine learning algorithms for hypothyroidism prediction and suggests that Random Forest algorithm can be a good choice for this task. This report presents a study on the prediction of hypothyroidism using body symptoms as inputs. To determine the likelihood that a patient has hypothyroidism, the algorithm makes use of a variety of demographic and symptom data. The program then makes recommendations for nearby hospitals based on the patient's district if the prediction is positive. It determines the most accurate and efficient method for predicting hypothyroidism and then mapping the patient location to the nearest hospital that has the capability to treat hypothyroidism.