The Role of Big Data Analytics in Healthcare

Khan, Nek Dil; Younas, Muhammad; Khan, Muhammad Taimoor; Duaa,; Zaman, Adil

doi:10.35940/ijsce.a3523.0911121

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…If they narrow in the middle, it is crepuscular. If they narrow in the tip, it is too bright [21][22][23][24]. Histogram of CXR image at COVID-19 patient showed narrow range distribution at the beginning which presented dull image, while in the negative case, the patient showed wide range distribution which presented flawless image.…”

Section: Image Extractionmentioning

confidence: 99%

Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images

Sumarti,

Sabrina,

Triana

et al. 2022

J. Penelit. Fis. Apl.

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Since the last five years of the COVID-19 outbreak, radiological images, such as CT-Scan and Chest X-Ray (CXR), have become essential in diagnosing this disease. However, limited access to facilities such as CT-Scanners and RT-PCR makes CXR images the primary method for COVID-19 testing. This research aims to improve the accuracy of CXR images in identifying COVID-19 patients based on the texture features: histogram and Gray Level Co-occurrence Matrix (GLCM), using the K-Means Clustering method. This study utilized 150 CXR images, including 75 COVID-19 patients confirmed by RT-PCR tests, and 75 patients with negative cases. The method used were consisted of pre-processing, and texture feature extraction with the seven most influential attributes based on gained information (histogram: standard deviation, entropy, skewness, kurtosis, and GLCM: correlation, energy, homogeneity), as well as classification using K-Means clustering methods. The results showed that the classification’s accuracy, sensitivity, and specification are 92%, 91%, and 93%, respectively. This image processing technique is a promising as well as a complementary tool in diagnosing COVID-19 cases, based on CXR images with lower costs and more reliable results.

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Section: Image Extractionmentioning

confidence: 99%

Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images

Sumarti,

Sabrina,

Triana

et al. 2022

J. Penelit. Fis. Apl.

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Development and Evaluation of Community Affairs Management Information System for Municipality of Torrijos

Reginio,

Madriño,

Robles

2024

Preprint

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A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

B. Sable,

Kapse

2024

IJEAT

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Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centered health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world on a daily basis. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilized to identify the most probable disease associated with a patient's symptoms. By logging into the system, a doctor can retrieve and review their patient's information and reports within the doctor's module. Physicians have the ability to analyze the patient's browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator has the ability to incorporate additional disease information, such as the type of disease and its symptoms. The data mining system runs based on the condition's name and symptoms. The administrator has access to the database including information on diseases and symptoms. Recommender systems employ diverse machine learning techniques in many domains, such as the healthcare recommendation system (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.

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The Role of Big Data Analytics in Healthcare

Cited by 4 publications

References 17 publications

Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images

Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images

Development and Evaluation of Community Affairs Management Information System for Municipality of Torrijos

A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

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