Exploring post-COVID-19 health effects and features with advanced machine learning techniques | Scientific Reports – Nature.com

In the last two and a half years, the COVID-19 pandemic has drastically affected millions worldwide. The impact hammers on physical and mental health problems in the post-COVID-19 state1. This phenomenon raises the necessity to investigate the relationship between post-COVID conditions and mental health2. Primarily, the investigation shows that coronavirus has a long-term effect of post-COVID-19 disease on sleep and mental illness, which also opens the door to detecting possible relationships between the severity of COVID-19 at the onset and sleep and mental illness3. Coronavirus affects the brain by bypassing the blood-brain barrier (BBB) in blood or via monocytes which could reach brain tissue via circumventricular organs7. Importantly, research shows a prominent frequency of impaired performance across cognitive domains in post-COVID patients with subjective complaints25. At the same time, the discovery of inflammatory biomarkers in COVID-19 survivors has come into broad light through MRI samples and other means4. One out of five patients hospitalized for COVID-19 was diagnosed with PTSD or subthreshold PTSD at a 3-month follow-up6. Potential contributing factors cause post-COVID-19 patients to suffer from different memory complaints5. Moreover, some psychiatric issues like depression prevail in COVID recovery patients, which causes a 25 times greater risk for suicide than the general population26. A summary of data from last year about the impacts on physical, cognitive, and neurological health disorders in COVID-19 survivors suggests three crucial aspects to manage: nutritional status, neurological disorders, and physical health28. So, the impaired cognitive deficits and emotional distress among COVID-19 patients should be addressed by functional rehabilitation27. Side by side, a brief study is to be analyzed on post-COVID-19 pandemic era mental health issues, vulnerable populations, and risk factors, as well as recommending a universal approach for mental health care and services29. Physiological and Neurological factors have been examined, with 39% classified as Physiological and 61% as Neurological. Neurological factors influence the mind and are connected to a persons mental and emotional state.30. Here anxiety is a major Neurological factor among post-COVID patients with a frequency rating of 8 as shown in Table 2. Anxiety is the most common mental illness in post-COVID1. Physiological factors deal with the functions of a living organism and its parts30. Fatigue is one of the most frequent alterations of post-COVID patients as shown in Table 2. Over the past three years, extensive research has explored physiological and neurological health complications in the aftermath of COVID-19. We reviewed 23 research articles using keywords like mental health, cognitive impairment, and post-COVID trauma. From these studies, we identified 17 health factors associated with COVID infection, including fatigue, forgetfulness, and anxiety. These factors were categorized into two groups: Physiological and Neurological. Notably, 39% are Physiological factors, while 61% are Neurological factors, impacting the mind and emotional well-being30. Here anxiety is a major neurological factor among post-COVID patients with a frequency rating of 8 as shown in the Table 2. Anxiety is the most common mental illness in post-COVID1. Physiological factors deal with the functions of a living organism and its parts30. Fatigue is one of the most frequent alterations of post-COVID patients Table 2.

In this way, all revealed health factors are listed in Table 2 along with references and frequency of presence in those references.

Among the 17 factors we have divided them into two categories, as shown in Table 2;

Physiological factors: Physiological factors deal with the functions of a living organism and its parts30. For example, fatigue is one of the most frequent alterations of post-COVID patients in Table 2. There are 7 physiological factors identified among all post-COVID-19 factors in this study, as shown in Table 2.

Neurological factors: Neurological factors are the one that influences or affects the mind and are related to the mental and emotional state of a person30. For example, anxiety is the most common mental illness in post-COVID1. There are 10 neurological factors identified among all post-COVID-19 factors in this study, as shown in Table 2.

We have given a statistical overview of our data in Fig.2 to make our data more understandable. Data statistics, such as count, min, max, mean, standard deviation, variance, and median, are essential for understanding a dataset. Count shows dataset size, min/max indicates its range, mean reflects central tendencies, standard deviation measures data spread, and variance quantifies overall variability. The median is a robust central measure. These stats form the foundation for data summary, with quartiles, percentiles, skewness, and kurtosis for deeper dataset analysis.

Statistical overview of data.

Feature correlation in Figs.3 and 4 gives a statistical measure that assesses the degree of association or relationship among features (variables) in our dataset. It quantifies how these features tend to vary together, providing insights into their dependencies. The advantages of this feature correlation (pearson) analysis in Fig. 4 (Full information is shown in Fig.5) includes its utility in identifying redundant or highly informative features for best model performance, detection of multicollinearity in regression analysis, simplifying data exploration by revealing hidden patterns and relationships, aiding in model interpretability, and facilitating feature engineering by leveraging the knowledge of feature associations to create new informative variables. Pearson correlation, is a crucial data science tool. It quantifies the strength and direction of the linear relationship between two continuous variables, with values ranging from 1 to 1. This technique is widely employed in statistics and data analysis to uncover connections, patterns, and dependencies within complex datasets.

Pearson correlation value for all to all input features.

Overview of target classanxiety.

TNSE visualization of features for after anxiety.

The chi-square test is one of the methods to find out the association i.e. relationship among the categorical variables. The relationship can be significant or insignificant. The standard P-value is considered as 0.05 and any p-value having less than 0.05 is considered to have a significant association i.e. relationship among variables as shown in Fig.3. In this research, the survey dataset has the responses i.e. level of impact on various physiological & neurological factors. These factors are considered categorical variables. The chi-square test is applyed on all factors and we got P-value for them which is shown in Fig.3. In the Table 3, calculated p-values less than 0.05 are marked with Grey color. These values with corresponding Factors are analysed to possess significant relationships among them.

From the Fig.3, we can see all comparing factors have an association between them, Some basic features association as follows: a. Chest Pain & Unhappiness b. Unhappiness & Forgetfulness c. Depression & vigilance d. Chest pain & confidence e. Confidence & vigilance f. Energy & confidence g. Sleep & attentiveness h. Attentiveness & vigilance i. Sleep & determination j. Determination & vigilance and k. Fear of COVID & energetic

Pearson correlation coefficient is a unit measuring the strength of the linear relationship between two variables. This is represented as the r-value. R-value results in the range from 1 to 1. +1 represents the positive correlations(direct relationship), 0 shows no relationship & 1 represents the negative correlations(inverse relationship). In the research, the physiological & neurological factors of the dataset are depicted as variables. The Pearson correlation coefficient is calculated for all factors, and we got the R-value for them shown in Fig.3. The R-values above 0.05 are considered for positive/direct relation between the factors. This means an increase in one factor may influence and increase the degree of another factor. R-values below 0(in the -ve range) are considered for Inverse relation between factors. This means a Decrease in one factor may influence and Decrease another factor. The Pearson correlation revealed a strong positive relationship between the two variables, with a correlation coefficient of 0.85, indicating a significant and direct association.

Feature importance analysis shown in Fig.3 using the Ordinary Least Squares (OLS) regression model is a valuable technique in data analysis and predictive modeling. In this table, we renamed each feature name and labeled it from 1 to 13. In the context of feature importance, OLS can reveal the impact of each independent variable on the dependent variable. Larger coefficient values indicate stronger feature importance, while coefficients near zero suggest less relevance. This analysis aids in feature selection, helping us focus on the most influential variables for building predictive models or understanding the factors that drive specific outcomes in the data. Based the outcome shown in Table 3, the most important feature is 13(with a score of 1.5447) and the less important feature is 1(with a score -1.0443).

Training algorithm for anxiety analysis.

Firstly, the compiled dataset is used for Statistical Analysis to explore whether any impact exists on the factors due to COVID-19 or not. The dataset possesses the info of both the Before and After conditions of the factors. The x-axis shows the categories/responses of people on how much each factor, like anger, depression, etc is affected. Y-Axis shows the percentage of how many persons are acknowledged in each category. In Fig.4b, we present a comparative view of anxiety before and after COVID-19. The blue color represents the degree of impact for the factors before being affected by COVID-19. The red color represents the status after suffering from the disease.

Before COVID-19 state, no people strongly agreed on having Anxiety over their COVID issue, but the percentage jumped to 16.67% who strongly agreed after suffering from it. The graph follows the same pattern in the subsequent remarks. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, a large number of people got the new problem whereas the people having previous Anxiety issues remained the same/more. In Fig. 4a, we present a complete view of anxiety amount before and after COVID-19.

It is such a factor that shows most of the patients are suffering from depression more after COVID. 23.33% and 36.67% patients either strongly agreed or agreed respectively on this matter. This figure has risen from 16.67% and 20.00% before COVID. While 36.67% disagreed on this matter before COVID the figure came down to only 10.00% after COVID. Depression, in human life, has increased after COVID-19

On the factor of unhappiness, 33.33%, and 26.67% people agreed on their unhappy life before and after COVID respectively. However we see an almost inverse trend on the neutral point of view among the patients. Thus comparing the before & after situation, it can be visualized that after suffering from COVID-19, unhappiness has decreased among the patients.

The degree of confidence before and after the COVID-19 era shows a drastic change in peoples mentality. Before COVID-19 state, 56.67% of people agreed on their degree of confidence but COVID had hit hard on their lifestyle shifting down to 20% confidence degree after COVID. The same trend was seen in the disagreement chart. Comparing the before & after situation, it can be concluded that after suffering from COVID-19, the majority of the peoples confidence in themselves was shattered.

Regarding forgetfulness, double the number of patients either agreed or strongly agreed that they forgot things now more after suffering from it. Thus, COVID has fatally affected the patients memory, resulting in curbing their brains.

Before suffering from COVID, about 60% people agreed that they were more patient in life, but the percentage abruptly dropped to half who decided to be after suffering from COVID. But none Strongly Disagreed in this regard, neither before nor after. Thus comparing the before & after situation, it can be visualized that after suffering from COVID-19, vigilance has decreased by almost half or beyond among the patients.

Before the COVID-19 state, most people (56.67%) agreed about being more energetic, whereas the percentage increased in favor disagreement (36.67% disagree, 10% strongly disagree) in the post-COVID state. Comparing the before & after situations, it can be depicted that after suffering from COVID-19, people are becoming significantly less energetic.

Before COVID-19 state, no people strongly agreed about having chest pain, but the percentage jumped to 23.33% who strongly agreed after suffering from COVID. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, a large number of people got the new problem, whereas the people having previous chest pain history remained the same/more.

Before COVID-19 state, about 36.67% of people agreed that they experienced more sleep, but the percentage decreased to 33.33% who agreed after suffering from COVID. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, experiencing sound sleep conditions shows a sight-decreasing tendency.

Before COVID-19 state, about 43% of people were NEUTRAL about their anger problem, whereas 40% people agreed about the problem. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, most people agreed that their anger has increased.

Before the COVID-19 state, most people (50%) disagreed about having dizziness problems, but the percentage is rising in favor of strongly agree (16.67%) and agree (36.67) in the post-COVID state. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, dizziness is slowly increasing among people after COVID.

Before the COVID-19 state, a few people (3.33%) strongly agreed that they had been impulsive, but the percentage increased to 20% who strongly agreed after suffering from COVID. Comparing the before & after situations, it can be concluded that after suffering from COVID-19, people show a sight-increasing impulsiveness tendency.

Before suffering from COVID, about 60% of people agreed that they were more vigilant, but the percentage abruptly fell to 16.67% who agreed after suffering from COVID. At the same time, disagreement degrees increased in the post-COVID situation. Comparing the before & after situations, it can be visualized that after suffering from COVID-19, vigilance has decreased dramatically among the patients.

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Exploring post-COVID-19 health effects and features with advanced machine learning techniques | Scientific Reports - Nature.com