Machine learning-based random forest for predicting decreased quality of life in thyroid cancer patients after thyroidectomy.

Abstract

Decreased quality of life (QoL) in thyroid cancer patients after thyroidectomy is a common, but there is a lack of predictive methods for decreased QoL. This study aimed to construct a machine learning-based random forest for predicting decreased QoL in thyroid cancer patients 3months after thyroidectomy. Two hundred and eighty-six thyroid cancer patients after thyroidectomy were enrolled in this prospective cross-sectional study from November 2018 to June 2019, and were randomly assigned to training and validation cohorts at a ratio of 7:3. The European Organization for Research and Treatment of Cancer quality of life questionnaire version 3 (EORTC QLQ-C30) questionnaire was used to assess the QoL 3months after thyroidectomy, and decreased QoL was defined as EORTC QLQ-C30 < 60 points. The random forest model was constructed for predicting decreased QoL in thyroid cancer patients after thyroidectomy. The mean QoL 3months after thyroidectomy was 65.93 ± 9.00 with 21.33% (61/286) decreased QoL. The main manifestation is fatigue in symptom scales and social functioning dysfunction in functional scales. The top seven most important indices affecting QoL were clinical stage, marital status, histological type, age, nerve injury symptom, economic income and surgery type. For random forest prediction model, the areas under the curve in the training and validation courts were 0.834 and 0.897, respectively. The present study demonstrated that random forest model for predicting decreased QoL in thyroid cancer patients 3months after thyroidectomy displayed relatively high accuracy. These findings should be applied clinically to optimise health care.