An exploratory study to predict recurrence of papillary thyroid carcinoma using clinico‑pathological factors with different machine learning algorithms in a tertiary care hospital

·        The data will be collected from the Nuclear Medicine department and Medical Records Department at Kasturba Hospital and extracted into a spreadsheet.

·        Following data extraction, data preprocessing is done to address the missing values or inconsistencies in the dataset.

·        After preprocessing the data, feature selection task will be carried out.

·        The extracted data will then be split into separate training and testing datasets.

·        Appropriate machine learning algorithms will be selected based on the features chosen from the dataset.

·        The model will be trained using the training dataset to learn patterns and relationships in the data.

·        After training, the model’s performance will be assessed using the validation dataset to ensure its accuracy.