Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis

Levshinskii, Vladislav; Polyakov, Maxim; Losev, Alexander; Хоперсков, А. В.

doi:10.1007/978-3-030-29750-3_35

Cited by 9 publications

(15 citation statements)

References 20 publications

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“…However, in general, the results obtained show high sufficiently computer models adequacy degree the obtained in [5][6][7], the possibility of their use in a hybrid method development for diagnosing breast cancer based on microwave radiothermometry data.…”

Section: Discussionsupporting

confidence: 53%

“…First of all, the need arose to develop adequate computer physical and mathematical models for studying the spatial and temporal temperature fields dynamics in the biological mammary gland tissues. In recent years, during theoretical research, a number of mathematical models have been created that describe the temperature distribution in human organs [4][5][6][7]. Including in works [5][6][7], when modeling, the main macroscopic factors determining thermal dynamics were taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, during theoretical research, a number of mathematical models have been created that describe the temperature distribution in human organs [4][5][6][7]. Including in works [5][6][7], when modeling, the main macroscopic factors determining thermal dynamics were taken into account. Instead of traditionally used models with homogeneous parameters in a multilayer approximation (usually limited to four tissue types -skin, muscles, mammary gland, tumors), a close to realistic geometric tissues structure with heterogeneous characteristics was used in the simulation.…”

Section: Introductionmentioning

confidence: 99%

“…In real conditions, the parameters values spread lies within very wide limits. Also in the study, based on in-depth medical data analysis and using machine learning algorithms, an algorithm for validating a simulation model was proposed [6].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

Polyakov¹,

Popov²,

Losev³

et al. 2021

MPCS

Self Cite

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This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the spacetime temperature distribution using the microwave radiothermometry method and obtained data intelligent analysis. The article deals with the machine learning application in the microwave radiothermometry data analysis. The problems associated with the construction mammary glands temperature fields computer models for patients with various diagnostics classes, are also discussed. With the help of a computer experiment, based on the machine learning algorithms set (logistic regression, naive Bayesian classifier, support vector machine, decision tree, gradient boosting, Knearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.

show abstract

Section: Discussionsupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

Polyakov¹,

Popov²,

Losev³

et al. 2021

MPCS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Current medical studies are on Data Mining [1], Artificial Intelligence (AI) [2], Machine Learning, and Deep Learning ranging from processing medical images on radiological data to early diagnosis issues [3,4]. Examples of intelligent solutions for malignant diseases such as heart disease, cancer, and diabetes are also encountered [5,6]. In these studies, more than 85,000 patients were analyzed to find confidential information; therefore, these studies could only be applied via data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux Disease

Doğan

Ridaoui

2021

Sakarya University Journal of Science

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Digitalization spreads day by day around the world; thus, the amount of data collected is on the rise. An increasing amount of data leads us to use the data and get the advantage of it by using methods like Data mining. Data mining is used in several industries. Especially as medical data is essential to be understood, it is crucial to work on it. Reflux disease is a painful illness spreading around the world. Reflux is more common compared to formerly known numbers of patients. Even though reflux is not as fatal as cancer, it decreases the quality of life and makes many people suffer in their daily life. So, reflux is affecting mental health directly. If we can ease the process of diagnosis of reflux, we may provide a better quality of life for people. In this study, various data mining algorithms are applied, and it is seen from results that medical care can be improved by changing. Nowadays, artificial intelligence applications in the field of gastroenterology stand out in various sources in the literature. However, a large database required that is specific for Reflux disease to implement these applications is available only at the Reflux Research Center in Ege University in Turkey. By benefiting the Short Form36 and Quadrad12 questionnaire data in this database, 3,909 patients and many artificial intelligence algorithms were used to discover the hidden associations among responses in the quality of life of these patients. The algorithms used in the tests are Apriori, Frequent Pattern Growth, Density-Based Spatial Clustering of Applications with Noise, Self-Organizing Map, and KMeans. In the tests, it was observed that the most successful algorithm in terms of the structure of the data was KMeans, and a set of remarkable 27 rules according to the optimal Sum of Square Error value was obtained.

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Assessment of the Spatial Resolution and Accuracy of Temperature Measurements in Radiothermometry of the Breast

Polyakov,

Khoperskov,

Agasieva

et al. 2023

Biomed Eng

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Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis

Cited by 9 publications

References 20 publications

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux Disease

Assessment of the Spatial Resolution and Accuracy of Temperature Measurements in Radiothermometry of the Breast

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