Big Data and Artificial Intelligence for E-Health

Sakly, Houneida; Said, Mourad; Seekins, Jayne; Tagina, Moncef

doi:10.1007/978-3-030-96814-4_23

Cited by 2 publications

(1 citation statement)

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“…The use of medical procurement data in clinical research and management has increased dramatically, but the missing value resources pose data cleaning challenges (Sakly et al, 2022). Although nowadays there are many popular data cleaning tools in various domains, they often process all variables uniformly (Shi et al, 2021), meaning that they might not serve well for medical procurement database because there is specific information to the variables that must be considered.…”

Section: Introductionmentioning

confidence: 99%

Data Cleaning in Medical Procurement Database: Performance Comparison of Data Mining Classification Algorithms for Tackling Missing Value

PENTRAKAN,

CHEN

2023

EPSTEM

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Data cleaning is an important process for improving the quality of decision-making information.One of today's popular cleaning tools is data mining techniques. In this paper, we focused on using data mining classification algorithms to resolve missing values in medical purchasing databases. To serve this purpose, the predictive performance of four different classifiers: Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Support Vector Machine (SVM) were compared in this study. We used 2,311 medical data records from procurement database in Thailand between July 2019 and December 2019 in the experimental process. We also discussed the function of feature selection and test options that support analysis to improve model performance. The results showed that the SVM algorithm outperforms with a maximum accuracy of 89.61%. Additionally, we discussed the strengths and weaknesses of these data mining techniques for data cleaning and future research.

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Section: Introductionmentioning

confidence: 99%

Data Cleaning in Medical Procurement Database: Performance Comparison of Data Mining Classification Algorithms for Tackling Missing Value

PENTRAKAN,

CHEN

2023

EPSTEM

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Advancements in Medical Robotics and AI-Assisted Diagnostics

Sudha,

Balakrishnan,

Varun

et al. 2024

AI-Driven Innovations in Digital Healthcare

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Recent advances in medical robotics, AI-assisted diagnostics, and data-driven technology have changed healthcare. This chapter analyses these advances, focusing on AI-assisted diagnostic technology's essential concepts, terms, and ideas and computer vision's vital role in healthcare. It examines how robotics, diagnostics, machine learning, deep learning frameworks, smart sensors, and the internet of things are applied in e-health. The chapter covers cardiac disease prediction, image processing, and improved 3D medical imaging to show how new technologies are changing diagnosis and therapy. It also underlines the challenges of implementing big data and cloud-based solutions and the links between AI and IoT in geriatric fall detection and medical RPA. The chapter highlights the transformative potential of these technologies to shape the future of healthcare and examines cybersecurity solutions tailored to medical IoT (MIoT). In result, this research sheds light on medical technology's fast evolution and the role of AI, robotics, and data-driven methodologies in reinventing healthcare.

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Big Data and Artificial Intelligence for E-Health

Cited by 2 publications

References 71 publications

Data Cleaning in Medical Procurement Database: Performance Comparison of Data Mining Classification Algorithms for Tackling Missing Value

Data Cleaning in Medical Procurement Database: Performance Comparison of Data Mining Classification Algorithms for Tackling Missing Value

Advancements in Medical Robotics and AI-Assisted Diagnostics

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