Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity

Abstract

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

Keywords: COVID-19; adverse reactions; comorbidities; machine learning; statistical analysis; symptoms; vaccination.

Figure 1

The schematic diagram of the overall workflow including data processing, data division, analysis using statistical and machine learning methods, and, at the end, performance evaluation with finding significant features.

Figure 2

Pearson’s correlation heat-map for the dataset medical history.

Figure 3

Pearson’s correlation heat-map for the dataset adverse reactions.

Figure 4

The significant features within the patients’ medical history, where the higher bar length indicates greater the significance.

Figure 5

The significant patients’ adverse reactions after vaccination, where the higher bar length indicates greater the significance.

Figure 6

Area Under the ROC curves for the machine learning model evaluation. (A). classification of died patients’ using patients’ medical history dataset; (B). classification of SARS-CoV-2 positive patients’ using patients’ medical history dataset; (C). classification of hospitalised patients’ using patients’ medical history dataset; (D). classification of died patients’ using patients’ reaction dataset; (E). classification of SARS-CoV-2 positive patients’ using patients’ reaction dataset; (F). classification of hospitalized patients’ using patients’ reaction dataset.

Figure 7

The features ranking according to the coefficient values of the patients’ medical history, calculated after machine learning model training. ML model outcomes indicate that higher coefficient values are mostly close to the significant association of severity.

Figure 8

The features ranking according to the coefficient values of the patients’ adverse reactions, calculated after machine learning model training. ML model outcomes indicate that higher coefficient values are mostly close to the significant association of severity.