This repository contains the dataset used in the paper "Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data" published in Scientific Reports. Please check our formal publication for the full details. The dataset contains 1232 nodules from 724 patients. Each row represents one nodule and each column represents one variable that describes the characteristics of the patient or nodule. The meaning of each variable is summarized below. id: the unique identity of the patient who carries the nodule age: the age of the patient FT3: triiodothyronine test result FT4: thyroxine test result TSH: thyroid-stimulating hormone test result TPO: thyroid peroxidase antibody test result TGAb: thyroglobulin antibodies test result site: the nodule location, 0: right, 1: left, 2: isthmus echo_pattern: thyroid echogenicity, 0: even, 1: uneven multifocality: if multiple nodules exist in one location, 0: no, 1: yes size: the nodule size in cm shape: the nodule shape, 0: regular, 1: irregular margin: the clarity of nodule margin, 0: clear; 1: unclear calcification: the nodule calcification, 0: absent, 1: present echo_strength: the nodule echogenicity, 0: none, 1: isoechoic, 2: medium-echogenic, 3: hyperechogenic, 4: hypoechogenic blood_flow: the nodule blood flow, 0: normal, 1: enriched composition: the nodule composition, 0: cystic, 1: mixed, 2: solid multilateral: if nodules occur in more than one location, 0: no, 1: yes mal: the nodule malignancy, 0: benign, 1: malignant

{"references": ["Xi, M.N., Wang, L., and Yang, C. (2022). Improving the Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data. Scientific Report 12, 1143"]}