# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 1 Introduction  
The acute manifestations of coronavirus disease 2019 (COVID-19) infection have been well established since the 2019 pandemic; long-term complications affect millions of people globally but are yet ill defined. Several names have been adopted for these sequelae including Long COVID-19, post-COVID-19 condition or syndrome, and Long Haul COVID-19 [1, 2, 3]. Long COVID-19 is a largely poorly understood clinical entity wherein relatively non-specific symptoms occur or persist after acute COVID-19 infection, akin to a post-viral syndrome. Several studies have attempted to define it, leaving a few accepted definitions by major institutions including World Health Organisation (WHO) and National Institute for Health and Care Excellence (NICE). Most definitions outline the following parameters for their criteria, exemplified below by excerpts from the WHO definition that was used to code Long COVID-19 in the tenth edition of the _International Classification of Diseases_ (ICD-10):  
1. Past COVID-19 infection: history of probable or confirmed COVID-19 infection;
2. Onset of symptoms: usually three months from onset of COVID-19 symptoms;
3. Duration of symptoms: symptoms lasting for at least two months;* Diagnosis of exclusion: symptoms cannot be explained by alternative diagnoses [1; 2; 3; 4]. There are a plethora of symptoms involving multiple systems which may be experienced, including fatigue, joint and muscle aches, flu-like or respiratory symptoms and neurological disturbances [5; 6]. Some definitions also further characterise the condition by describing how symptoms may persist following acute infection or new symptoms may develop, symptoms may fluctuate with time, and symptoms typically cause a level of functional impact [2; 4]. The first reports of Long COVID-19 emerged early in the pandemic in 2020. Estimates of prevalence vary significantly among studies, largely due to the lack of a strict definition of Long COVID-19, particularly with studies differing in onset of Long COVID-19 symptoms following acute COVID-19 infection [3; 7]. A review by the Australian Institute of Health and Welfare demonstrated an estimated 5% to 10% prevalence of Long COVID-19 in Australia, with prevalence among global data ranging from 9% to 81% [7]. Whilst the exact prevalence is unknown, the consensus in the literature is that Long COVID-19 has likely impacted an extensive portion of the population, having an incidence of approximately 10% among COVID-19 cases [1; 5]. The prevalence of Long COVID-19 among COVID-19 cases has also been shown to be greater than that of other post-viral syndromes following non-COVID-19 respiratory infections [8]. Furthermore, Long COVID-19 has been associated with increased morbidity and decreased quality of life [1; 9], as well as increased financial burden for patients and healthcare systems due to the subsequent long-term management and follow-up required [7]. Complete recovery is also uncommon, with up to 85% of patients reporting symptoms persisting past one year [1; 5]. With the potential for such a significant long-term impact, understanding risk factors for developing Long COVID-19 is thus key in facilitating risk stratification of patients, which can guide management and preventative measures to reduce associated morbidity and healthcare burden. Thus far, risk factors are likewise largely ill-defined, with those that are well established in the literature being primarily demographic and clinical in nature. Several studies including two large meta-analyses in 2023 and 2024 have demonstrated strong evidence for two of the most consistently identified risk factors for Long COVID-19: female sex and middle age (between 40 and 65 years, approximately). Other high-risk groups include those with underlying comorbidities such as asthma and diabetes, their associated risk factors such as smoking and obesity, as well as mental health conditions.