# Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach

## TL;DR

This study uses machine learning to predict long-term effects of COVID-19 in cancer patients, helping doctors provide better care.

## Contribution

The study introduces a machine learning approach to predict post-COVID conditions in hospitalised cancer patients.

## Key findings

- KNN achieved the highest predictive performance with an AUC of 0.80.
- Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC.
- Machine learning models can aid in early intervention and personalized care for cancer patients.

## Abstract

This study investigates the long-term effects of COVID-19 on cancer patients, who are more vulnerable due to weakened immune systems. Many individuals experience lingering symptoms, referred to as PCC, even after recovering from the initial infection. To better understand this condition, the researchers analysed data from hospitalised cancer patients in Moscow, aiming to predict which patients are most at risk of developing PCC. By applying machine learning models, they identified patterns that can help clinicians detect and manage these long-term symptoms. The findings contribute to improving care for cancer patients affected by COVID-19 and offer insights that could aid the broader medical community in understanding the long-term impacts of the virus.

Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia. Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge. A total of 49 clinical features were evaluated, and machine learning classifiers including logistic regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and neural network were applied to predict PCC. Results: Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. KNN demonstrated the highest predictive performance, with an AUC of 0.80, sensitivity of 0.73, and specificity of 0.69. Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC. Conclusions: Machine learning models, particularly KNN, showed some promise in predicting PCC in cancer patients, offering the potential for early intervention and personalised care. These findings emphasise the importance of long-term monitoring for cancer patients recovering from COVID-19 to mitigate PCC impact.