Classification of Cardiovascular Disease Using AdaBoost Method

Bazilevych, Kseniia; Butkevych, Mykola; Padalko, Halyna

doi:10.1007/978-3-031-20141-7_11

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Evaluating informativeness is essential for analyzing epidemic process data, as it allows for determining the significance of various factors and relationships associated with diseases [14]. This helps to identify key factors affecting the spread of epidemics and make effective decisions regarding their prevention and treatment.…”

Section: Introductionmentioning

confidence: 99%

Information system for assessing the informativeness of an epidemic process features

Bazilevych,

Kyrylenko,

Parfenyuk

et al. 2023

SRIT

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The primary objective of this study is to assess the informativeness of various parameters influencing epidemic processes utilizing the Shannon and Kullback–Leibler methods. These methods were selected based on their foundation in the principles of information theory and their extensive application in machine learning, statistics, and other relevant domains. A comparative analysis was performed between the results acquired from both methods, and an information system was designed to facilitate the uploading of data samples and the calculation of factor informativeness impacting the epidemic processes. The findings revealed that certain features, such as “Chronic lung disease,” “Chronic kidney disease,” and “Weakened immunity,” did not carry significant information for further analysis and hindered the forecasting process, as per the data set examined. The developed information system efficiently supports the assessment of feature informativeness, thereby aiding in the comprehensive analysis of epidemic processes and enabling the visualization of the results. This study contributes to the current body of knowledge by providing specific examples of applying the described algorithmic models, comparing various methods and their outcomes, and developing a supportive tool for analyzing epidemic processes.

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Section: Introductionmentioning

confidence: 99%

Information system for assessing the informativeness of an epidemic process features

Bazilevych,

Kyrylenko,

Parfenyuk

et al. 2023

SRIT

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“…The world has accelerated the digitalization of most areas of activity, including healthcare systems [2]. Research related to datadriven medicine is aimed at solving such problems as automated diagnostics [3], analysis of medical [4] and nonmedical interventions [5] to reduce the dynamics of morbidity, analysis of medical images [6], analysis of medical data [7], and modeling the dynamics of the epidemic process [8].…”

Section: Introductionmentioning

confidence: 99%

“…(2) To develop a predictive model for COVID-19 dynamics based on the logistic regression method (3) To develop a predictive model of COVID-19 dynamics based on the decision tree method (4) To develop a predictive model for COVID-19 dynamics based on the support vector regression method (5) To evaluate the results of predicting the dynamics of COVID-19 using the developed models for data in various territories (6) To compare the accuracy and adequacy of the developed models performed with the databases of different countries (7) To analyze the performance of the developed models…”

Section: Introductionmentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

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This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

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“…The development of the pandemic has stimulated research groups around the world to direct their efforts to develop data-driven approaches aimed at combating COVID-19. Such studies included the analysis of medical data [7], the search for the informativeness of medical factors [8], methods of automated diagnostics [9], complex models of the dynamics of the epidemic process [10], methods of medical computer vision [11], assessment of factors affecting the epidemic process [12], the formation of strategies to stop the epidemic spread of morbidity [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion’s Effect on Italy’s COVID-19 Dynamics

Chumachenko,

Dudkina,

Chumachenko

et al. 2023

Computation

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Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, leading to healthcare disruptions, medical resource shortages, and heightened emergency care needs. Italy emerged as a significant refuge for displaced Ukrainians during this period. Aim: This research aims to discern the impact of the Russian full-scale invasion of Ukraine on the COVID-19 transmission dynamics in Italy. Materials and Methods: The study employed advanced simulation methodologies, particularly those integrating machine learning, to model the pandemic’s trajectory. The XGBoost algorithm was adopted to construct a predictive model for the COVID-19 epidemic trajectory in Italy. Results: The model demonstrated a commendable accuracy of 86.03% in forecasting new COVID-19 cases in Italy over 30 days and an impressive 96.29% accuracy in estimating fatalities. When applied to the initial 30 days following the escalation of the conflict (24 February 2022, to 25 March 2022), the model’s projections suggested that the influx of Ukrainian refugees into Italy did not significantly alter the country’s COVID-19 epidemic course. Discussion: While simulation methodologies have been pivotal in the pandemic response, their accuracy is intrinsically linked to data quality, assumptions, and modeling techniques. Enhancing these methodologies can further their applicability in future public health emergencies. The findings from the model underscore that external geopolitical events, such as the mass migration from Ukraine, did not play a determinative role in Italy’s COVID-19 epidemic dynamics during the study period. Conclusion: The research provides empirical evidence negating a substantial influence of the Ukrainian refugee influx due to the Russian full-scale invasion on the COVID-19 epidemic trajectory in Italy. The robust performance of the developed model affirms its potential value in public health analyses.

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Classification of Cardiovascular Disease Using AdaBoost Method

Cited by 4 publications

References 13 publications

Information system for assessing the informativeness of an epidemic process features

Information system for assessing the informativeness of an epidemic process features

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion’s Effect on Italy’s COVID-19 Dynamics

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