Visual analogue scale foot and ankle vs. short-form 36 quality of life scores: artificial intelligence using machine...

We aimed to utilize artificial intelligence (AI) via machine learning (ML) to analyze the relationship between visual analogue scale foot and ankle (VASFA) and short-form 36 (SF-36) quality of life scores and determine AI's performance over the aforementioned analysis.

We collected data from our registry of 819 data units or rows of datasets of foot and ankle patients with VASFA, SF-36 scores, and other demographic data. They were prepared and verified to be a proper input for building ML models using a web-based algorithm platform. After the first ML model was developed using random forest regression, the SF-36 percentage value was set as an endpoint. We developed a second ML model to evaluate it against the current algorithm. This new model employed a gradient-boosting regressor, where we omitted a key parameter, SF_Total, to correct the overfitting. We performed an external validation based on an unseen dataset from 42 data units of patients.

Internal validity showed an excellent relationship among the VASFA, SF-36 total score, and overall SF-36 percent values at a correlation coefficient (R2 score) of 1.000 based on the random forest regression model of ML (first model: 28XJ). The VASFA percent value of the total score (0=worst; 100=best) demonstrated the dynamic changes in the three zones of the score levels; these were unsatisfactory: ≤ 57.25; borderline: 57.26-80.99; satisfactory: ≥ 81 and could impact the levels of overall SF-36 percent value. A second ML model (model FK13) showed an R2 score of 0.977, which was a great performance. External validation showed no significant difference between the predicted and actual values, with a two-tailed p-value of 0.2136.

Our ML models predicted excellent relationships among VASFA, with or without SF-36 total score and overall SF-36 percentage values, with evidence from external validation.