# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 3 Results
### Model Results
\begin{table}
\begin{tabular}{l c} \hline \hline Sensitivity (recall, true positive rate) & 0.974 \\ \hline Specificity (true negative rate) & 0.654 \\ \hline False positive rate & 0.346 \\ \hline False negative rate & 0.026 \\ \hline Precision (positive predictive value) & 0.892 \\ \hline Negative predictive value & 0.895 \\ \hline Accuracy & 0.892 \\ \hline F1 score & 0.931 \\ \hline Matthews correlation coefficient & 0.702 \\ \hline Area under the receiver operating characteristic curve (AUROC) & 0.791 \\ \hline False discovery rate & 0.108 \\ \hline False omission rate & 0.105 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics. node purity (0.047), followed by loneliness (0.008), having an uncommon chronic health condition (0.008), hours worked per week (0.005), age (0.005) and financial security (0.003). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Predictor Variable** & **Total Increase in Node Purity** & **Mean Decrease in Accuracy** \\ \hline Timing of acute COVID-19 infection & 0.047 & 0.120 \\ \hline Feels lonely often & 0.008 & 0.011 \\ \hline Uncommon chronic health condition & 0.008 & 0.010 \\ \hline Number of hours worked per week & 0.005 & \(-0.008\) \\ \hline Age & 0.005 & \(-0.006\) \\ \hline Financial security in next 3 months & 0.003 & \(-0.001\) \\ \hline Job security in next 12 months & 0.002 & \(4.939\times 10^{-4}\) \\ \hline Amount of fruit eaten per day & 0.002 & \(-0.004\) \\ \hline Arthritis & 0.002 & \(-0.002\) \\ \hline Number of alcoholic drinks & 0.001 & 0.005 \\ typically consumed in a day & \(9.650\times 10^{-4}\) & \(-1.318\times 10^{-5}\) \\ \hline Common chronic health condition & \(9.372\times 10^{-4}\) & 0.004 \\ \hline Household size & \(9.360\times 10^{-4}\) & 0.003 \\ \hline Neuropsychiatric condition & \(7.889\times 10^{-4}\) & \(-0.001\) \\ \hline Ethnic group & \(7.773\times 10^{-4}\) & \(-0.003\) \\ \hline Household income bracket & \(6.385\times 10^{-4}\) & 0.001 \\ \hline BMI \(\geq\) 40 & \(4.676\times 10^{-4}\) & \(-0.001\) \\ \hline Diabetes & \(4.320\times 10^{-4}\) & \(2.604\times 10^{-4}\) \\ \hline Malignancy & \(3.094\times 10^{-4}\) & \(4.576\times 10^{-4}\) \\ \hline Food insecurity over past week & \(2.961\times 10^{-4}\) & \(-2.935\times 10^{-5}\) \\ \hline Asthma & \(2.667\times 10^{-4}\) & \(-8.832\times 10^{-4}\) \\ \hline Immunosuppressive medication & \(2.164\times 10^{-4}\) & \(1.407\times 10^{-4}\) \\ \hline At risk of serious illness from COVID-19 & \(1.472\times 10^{-4}\) & \(1.447\times 10^{-4}\) \\ \hline Cares for others outside the household & \(6.645\times 10^{-5}\) & \(-0.005\) \\ \hline Sex & \(-3.437\times 10^{-4}\) & \(-0.002\) \\ \hline No immunosuppressive treatment & \(-5.427\times 10^{-4}\) & \(6.861\times 10^{-4}\) \\ \hline Number of cigarettes smoked per day & \(-0.001\) & \(-8.703\times 10^{-4}\) \\ \hline Cares for others within & \(-0.001\) & \(-7.602\times 10^{-4}\) \\ \hline Had a COVID-19 vaccine & \(-0.002\) & \(0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Feature importance. Figure 3-8 are plots illustrating the proportion of the sample reporting one or more. Long COVID-19 symptoms on the y-axis for predictors with the highest increase in node purity in the model. Figure 2: Variable contribution to node purity. Figure 4: Proportion of sample reporting Long COVID-19 symptoms by whether they had an uncommon chronic health condition. Figure 5: Proportion of sample reporting Long COVID-19 symptoms by how often they report feeling, lonely. Figure 6: Proportion of sample reporting Long COVID-19 symptoms by number of hours worked per week.