# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 4 Discussion
### Identified Risk Factors for Long COVID-19  
The most significant predictors for Long COVID-19 in our model were timing of acute COVID-19 infection, having uncommon chronic health conditions, loneliness, hours worked per week, age and financial security, derived from the feature importance data as the predictors with the greatest increase in node purity (Table 5 and Figure 2). This result partially aligns with the existing literature. Firstly, timing of acute COVID-19 infection was the most significant predictor in our model, with a considerably greater contribution to node purity than the next most important feature (refer to Table 5 and Figure 2). In accordance with the literature, our model shows that participants who had COVID-19 earlier in the pandemic were more likely to report Long COVID-19 symptoms (Figure 3). This is also endorsed by findings from similar \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Total Increase in Node** & **Node Purity Rank in** \\  & **Purity** & **Main Analysis** \\ \hline Timing of acute COVID-19 infection & 0.016 & 1 \\ \hline Age & 0.009 & 5 \\ \hline Number of hours worked per week & 0.005 & 4 \\ \hline Financial security in next 3 months & 0.005 & 6 \\ \hline Feels lonely often & 0.004 & 2 \\ \hline Had a COVID-19 vaccine & 0.004 & 28 \\ \hline Hypertension & 0.004 & 11 \\ \hline Common chronic health condition & 0.003 & 12 \\ \hline Job security in next 12 months & 0.001 & 7 \\ \hline Asthma & 0.001 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Node purity increase statistics showing the features with the highest importance in the sensitivity analysis compared with their importance rank in the main analysis. studies that generate risk prediction models for Long COVID-19. A study by [PERSON] et al. also found COVID-19 variant to be the most influential feature in their gradient booster classification model, with a similar direction of effect [18]. Analysing the trend in more detail, Long COVID-19 risk in our model was highest early in the pandemic, with peak risk being approximately between the November 2020 and January 2021 survey waves, representing early wild-type COVID-19 variants. Long COVID-19 risk then decreases slightly in the next survey wave, which corresponds to the rise of the Delta variant. By the last survey wave, Long COVID-19 risk sharply declines to zero; this is due to missing responses, as the presence of Long COVID-19 was not yet assessable for participants who only reported acute infection in the last wave. [PERSON] et al. found similar results, with earlier wild-type COVID-19 variants being associated with highest risk of Long COVID-19, followed by the Delta variant. As their study included data until July 2022, they were able to capture further trends that begin to emerge in our model, with the more recent Omicron variant being associated with lowest risk of Long COVID-19 [18]. This is consistent with the current literature, with most data supporting lowest risk of Long COVID-19 following infection with Omicron variants, with risk increasing with the Delta variant and highest risk in the earlier wild-type variants [5; 7; 11; 13]. Having uncommon health conditions and loneliness were associated with a lower risk of Long COVID-19 (Figures 4 and 5, respectively). Our model included predictors for common health conditions that are among the more well-established risk factors for Long COVID-19 in the literature, including asthma, cancer, diabetes, hypertension, high BMI and neuropsychiatric conditions [5; 7; 8; 10], as well as in similar machine learning models and online cohort longitudinal studies [11; 15; 16; 17; 18]. Interestingly, it appears that the absence of these may be a stronger classifier of not having Long COVID-19 in our model, thus supporting the converse of the current literature and suggesting a possible protective effect. It is also important to note that whilst several of these comorbidities are becoming recognised as risk factors, there are still studies that report conflicting results.