Role of data mining and machine learning techniques in medical imaging

Agnihotri, Abhishek; Yadav, Vikash; Kaushik, Vandana Dixit

doi:10.1504/ijaip.2020.105838

Cited by 9 publications

(5 citation statements)

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“…Among them, acute angina pectoris is more common clinically and is one of the main factors that induce coronary heart disease [ 1 , 2 ]. At present, medical imaging has important clinical significance in guiding and planning before treatment, tracking and positioning during treatment, and prognostic progress [ 3 ]. Therefore, analyzing the clinical treatment of patients with acute angina in cardiovascular medicine from the perspective of medical imaging has important reference significance for clinical efficacy.…”

Section: Introductionmentioning

confidence: 99%

Clinical Treatment Analysis and Imaging Study of Patients with Acute Angina in Cardiovascular Medicine

2021

Journal of Healthcare Engineering

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With the accelerating pace of life, increasing stress and unhealthy diet make cardiovascular disease one of the important diseases that endanger human health, among which the incidence of acute angina is gradually increasing. At present, there are many clinical treatment studies on acute angina pectoris, but the relevant imaging analysis is very lacking. In order to study the clinical treatment of patients with acute angina pectoris and analyze the relevant medical images, to arrive at a more effective treatment method, this article launched an in-depth study. First, we selected 88 patients with acute angina in a hospital as the research object and randomly divided them into a control group (n = 44) and an experimental group (n = 44) Yan et al. (2020). The control group was treated with conventional acute angina pectoris drugs, while the experimental group was treated with clopidogrel on this basis. The two groups were treated at the same time, and the treatment time lasted for 3 months. Then, the risk factors of the two groups of patients were analyzed, and the differences were statistically significant ( P < 0.05 ). Then, the medical images of the two groups of patients were analyzed, and the diastolic blood pressure, systolic blood pressure, and coronary artery stenosis were compared before and after treatment. After treatment, the diastolic blood pressure and systolic blood pressure of the experimental group were 88.31 ± 3.15 mmHg and 125.63 ± 4.16 mmHg, respectively. The proportion of patients with zero-vessel disease and single-vessel disease in the experimental group increased to 15.91% and 56.82%. The treatment plan received by the experimental group patients had a better improvement effect. Finally, the clinical efficacy was compared. The total effective rates of the control and experimental groups were 72.7% and 88.6%, respectively. This shows that the treatment method adopted by the experimental group of patients has a better curative effect and is worthy of clinical promotion.

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Section: Introductionmentioning

confidence: 99%

Clinical Treatment Analysis and Imaging Study of Patients with Acute Angina in Cardiovascular Medicine

2021

Journal of Healthcare Engineering

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“…[19], [20], [21], [22], [23] Time Series Transformation Focuses on transforming time series data using machine learning techniques for augmentation, including methods for forecasting and analysis that enhance the richness of the dataset.…”

Section: Data Expansion Techniquesmentioning

confidence: 99%

Overview of Data Augmentation Techniques in Time Series Analysis

ANNAKI,

RAHMOUNE,

BOURHALEB

2024

IJACSA

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Time series data analysis is vital in numerous fields, driven by advancements in deep learning and machine learning. This paper presents a comprehensive overview of data augmentation techniques in time series analysis, with a specific focus on their applications within deep learning and machine learning. We commence with a systematic methodology for literature selection, curating 757 articles from prominent databases. Subsequent sections delve into various data augmentation techniques, encompassing traditional approaches like interpolation and advanced methods like Synthetic Data Generation, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). These techniques address complexities inherent in time series data. Moreover, we scrutinize limitations, including computational costs and overfitting risks. However, it's essential to note that our analysis does not end with limitations. We also comprehensively analyzed the advantages and applicability of the techniques under consideration. This holistic evaluation allows us to provide a balanced perspective. In summary, this overview illuminates data augmentation's role in time series analysis within deep and machine-learning contexts. It provides valuable insights for researchers and practitioners, advancing these fields and charting paths for future exploration.

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“…Thus, the advanced version of this cellular network 5G technology could enable students to learn both synchronously and asynchronously. 5G technology helps in boosting mobile learning in terms of high bandwidth, more capacity, more mobility, reliability and availability of network for learning in wireless classroom [32][33][34]. The 5G technology and its devices will also allow the students to learn with the better content visualization and attending class reducing signal lost and attenuation.…”

Section: Mobile Learning Compatibility With 5g Features Compatibilitymentioning

confidence: 99%

Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19

Gupta¹,

Kumar²,

Yadav³

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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INTRODUCTION:The novel corona disease disrupted education all around the world. This shifted people to mobile learning in real time wireless classroom from the physical face-to-face classroom. OBJECTIVE: Mobile learning has been present for years but the use of mobile learning is more in the current scenario due to COVID-19. However, people's acceptance of mobile learning education at institutions is still low. Thus, this research seeks to understand the student's perspective by analysing constructs hypothesized in the proposed hybrid model. METHOD: Data is collected using a survey from an Indian institute of the Meerut region with a total of 1022 students.RESULT: Data analysis and research findings showed that Random Forest and K-Nearest Neighbour Algorithms outperforms than other classifiers in predicting the dependent variables with better accuracy rate, precision, and recall value in this study. CONCLUSION:The research findings will help the designers and software development to design learning applications considering the perspective of students with respect to 5G technology.

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Role of data mining and machine learning techniques in medical imaging

Cited by 9 publications

References 0 publications

Clinical Treatment Analysis and Imaging Study of Patients with Acute Angina in Cardiovascular Medicine

Clinical Treatment Analysis and Imaging Study of Patients with Acute Angina in Cardiovascular Medicine

Overview of Data Augmentation Techniques in Time Series Analysis

Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19

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