# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 4 Discussion
### Identified Risk Factors for Long COVID-19
For example, a systematic review of diabetes as a risk factor for Long COVID-19 found that pre-existing diabetes was an identified risk factor for Long COVID-19 in 44% of the included studies, with no significant associations found in the remaining 56% and evidence for new-onset diabetes to be a complication of Long COVID-19 itself [22]. As for loneliness, those who reported feeling lonely more often may have been more isolated and hence less likely to develop acute COVID-19 infection; however, its relationship with Long COVID-19 is less intuitive. Rather, studies have postulated that loneliness may be a risk factor for Long COVID-19 due to its association with mental health conditions and socioeconomic deprivation and may lead to predominant neuropsychiatric symptoms [23; 24]. This and the limitations of self-reported data could explain our results, whereby lonelier patients may be less likely to self-report Long COVID-19 symptoms, as these more non-specific neuropsychiatric symptoms may be more likely to be misattributed to mental health conditions or loneliness itself, resulting in an apparent lower risk. However, in contradiction, two studies that also utilised self-reported survey data found pre-pandemic depression, anxiety, stress and loneliness to be associated with increased risk of Long COVID-19 [11; 25]. These findings thus suggest further analysis is required in the domain of medical and psychiatric comorbidities to elucidate the definite direction of their relationship with Long COVID-19. With respect to age, the model showed an increasing risk of Long COVID-19 with increasing age, with the highest risk being in the 55-80 age group (Figure 6). Our result is partially consistent with the existing literature, which typically reports middle-aged patients as the peak risk group, with older patients having equal or lower risk compared to younger age groups. A similar result to the literature was observed in the model by [PERSON] et al. [18]. This is thought to be due to higher mortality risk in older patients during acute COVID-19 infection, theorising that some may not survive to develop Long COVID-19 despite this age group being more likely to have other predisposing factors such as medical comorbidities, which pose higher risk of Long COVID-19 [10]. It is possible that our cohort has captured older patients who experienced milder acute infections given the nature of population-based data, which include both hospitalised and non-hospitalised patients, resulting in the discrepancy in the age group at highest risk. The main social determinants that proved significant in our model were hours worked per week and financial security. Working more hours per week--in particular, greater than 55 h--and financial insecurity were associated with higher Long COVID-19 risk (Figures 7 and 8, respectively). Both of these factors serve as markers of socioeconomic status, thereby supporting studies in the existing literature that have found evidence for lower socioeconomic groups being at higher risk [5; 7; 14]. Notably, lower socioeconomic status and financial insecurity were found to be associated with Long COVID-19 risk in a multivariable logistic regression model of an online longitudinal cohort [11]. In contrast to recent studies, there were also several variables that did not appear to be significant features in our algorithm, including sex, ethnicity, vaccination status, lifestyle factors and other markers of socioeconomic status (Table 5 and Figure 2). Typically, these variables would as such be removed from the analysis; however, on trialling this, it was found that the model performance significantly declined, with a much poorer recall value. This suggests that these variables may be resulting in some interaction effects with more significant variables despite their relatively insignificant independent importance.