The development of information technology in the modern world affects the public health sector on the one hand and accumulates enormous amounts of data on the other hand. The global COVID-19 pandemic has contributed to the digitalization of healthcare. Heart disease is a global problem that causes death worldwide. Therefore, this study proposes a model for determining the information content of signs of diagnostic data of heart diseases based on the cumulative frequency method. The software implementation of the completed. A database of 303 patients, consisting of 14 attributes, was used for the experiments. As a result of the model's work, the features with the most significant information content were identified. The study is promising and can apply diagnostic models in public health practice. Features informativeness, cumulative frequency analysis, medical diagnostics, heart disease, ORCID: 0000-0001-5332-9545 (K. Bazilevych); 0000-0001-8189-631x (M. Butkevych); 0000-0003-3570-5900 (N. Dotsenko)
Cardiovascular disease is the leading cause of adult death worldwide. Mortality reaches 30% of the total number of all deaths [1]. Cardiovascular diseases are congenital and acquired. The following are distinguished among cardiovascular diseases [2]: EMAIL: (K. Bazilevych); (M. Butkevych);
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The main danger of cardiovascular disease is the disability or sudden death. The likelihood of such consequences increases when ignoring the signs of the disease. Among the main risk factors are [3]:
Also, the causes of cardiovascular diseases include high blood pressure and diabetes. Therefore, early diagnosis is one of the most effective methods of preventing cardiovascular diseases.
The COVID-19 pandemic has stimulated research in the field of data-driven medicine to solve various problems. These areas include modeling the epidemic process of infectious diseases [4, 5], the study of molecular structures [6], the study of social factors affecting the spread of disease [7], the study of the behavior of viruses [8], medical diagnostics [9], etc.
However, the available data on the disease does not always allow the construction of high -quality models of automated medical diagnostics.
This study aims to determine the information content of signs for the diagnosis of cardiovascular diseases using the cumulative frequency method.
Given research is part of a complex intelligent information system for epidemiological diagnostics, the concept of which is discussed in [10].
Often the data sets to be processed contain a large number of features. When building machine learning models, it is not always clear which of the features are important for it and which are redundant [11]. At the same time, the removal of redundant data allows a better understanding of the data, as well as reducing the time for setting up the model, improving its accuracy and facilitating interpretability. Often this is the most important task. Feature selection methods are divided into three types: • Filter methods. • Embedded methods.
• Wrapped methods.
In the field of data-driven medicine, it is possible to recognize the presence or absence of a disease only when certain signs inherent in the patient are received and analyzed. Such signs are called informative [12]. But informative features are not equivalent to achieve a specific goal, so determining their informativeness is an important task.
Informativeness of a sign means how much this sign characterizes the state of the object, that is, how much the diagnosis depends on it - the result of recognition. At the same time, two approaches can be distinguished for determining the information content: energy and information.
The energy approach is based on the fact that the information content is estimated by the value of the feature. However, this approach may be poorly suited for object recognition. If some attribute is large in absolute value, but almost the same for objects of different classes, then it is difficult to attribute the object to a certain class by the value of this attribute. And if the attribute is relatively small in size, but differs greatly for objects of different classes, then the object can be easily classified by its value.
According to the informational approach, feature information is considered as a reliable difference between classes of images in the feature space. When classifying objects, such a significant difference can be the difference in the probability distributions of a feature built on samples from comparable classes. 2.2.
The essence of the cumulative frequency method is that if there are two samples of a feature x belonging to two different classes, then for both samples in the same coordinate axes, there are empirical distributions of the feature x [13]. The cumulative frequencies are calculated, i.e. the sum of frequencies from the initial to the current distribution interval. In this case, the module of the maximum difference of the accumulated frequencies serves as an estimate of information content: (1) ( ) = max | 1 − 2 |,
=0,.., where M1j is the cumulative frequency for the j-th sampling interval A1;
Experimental studies were carried out using the Python programming language. The open Heart Disease Cleveland dataset [14] was used for the analysis. The dataset contains data on 303 patients with 14 attributes. Attribute data is shown in Table 1.
Sex (1=male; 0=female)
1=trye; 2=false 0: normal; 1: having ST-T wave abnormality; 2: showing probable or definite left ventricular hypertrophy by
Estes’ criteria
exercise relative to rest
colored by flourosopy 3=normal; 6=fixed defect; 7=reversable defect 0: <50% diameter narrowing;
As a result, the information content was calculated for different groups of cardiological data. It was found that the following signs are the most informative: thal, chest pain type, colored vessels, angina, age. The cumulative frequency method is used to determine the information content of a feature involved in the recognition of two classes of objects.
The use of an automated software package developed in the framework of this study allows its use at workplaces in medical institutions to support decision-making when making a diagnosis. An automated solution is especially relevant in conditions of limited resources in low- and middleincome countries and during force majeure, such as war, natural disasters, and other conditions in which access to medical care is limited.
As a result of the study, an automated software package was used to determine the information content of the signs of these patients with suspected heart disease based on the accumulated frequency method. An open dataset of patients with suspected heart disease was used for experimental studies, which included 303 patients and 14 attributes. It was found that the following signs are the most informative: thal, chest pain type, colored vessels, angina, and age.
The proposed software package is highly relevant in Russia's war in Ukraine, as it does not require high computing power. At the same time, automating a doctor's diagnosis and decision -making support in conditions of limited resources is an urgent task.
The study was funded by the National Research Foundation of Ukraine in the framework of the research project 2020.02/0404 on the topic “Development of intelligent technologies for assessing the epidemic situation to support decision-making within the population biosafety management”. 6. References [1] M. Alessandro, P.E. Puddu, Epidemiology of heart disease of uncertain etiology: a population study and review of the problem, Medicina 55 (10) (2019): 687. doi: 10.3390/medicina55100687 [2] S.M. Hollenberg, Valvular heart disease in adult: etiologies, classification, and diagnosis, FP essentials 457 (2017): 11-16. [3] S.S. Virani, et. al., Heart disease and stroke statistics – 2020 update: a report from the American heart association, Circulation 141 (9) (2020): e139-e596. doi: 10.1161/CIR.0000000000000757 [4] D. Chumachenko, et. al., On agent-based approach to influenza and acute respiratory virus infection simulation, 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2018 – Proceedings (2018): 192-195. doi: 10.1109/TCSET.2018.8336184 [5] D. Chumachenko, On intelligent multiagent approach to viral hepatitis B epidemic processes simulation, Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018 (2018): 415-419. doi: 10.1109/DSMP.2018.8478602 [6] A.S. Tkachenko, et. al., Semi-refined carrageenan promotes generation of reactive oxygen species in leukocytes of rats upon oral exposure but not in vitro, Wiener Medizinische Wochenschrift 171 (3-4) (2021): 68-78. doi: 10.1007/s10354-020-00786-7 [7] N. Davidich, et. al., Monitoring of urban freight flows distribution considering the human factor,
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