scholarly journals Early Detection of Diabetic Retinopathy

2021 ◽  
Vol 9 (VI) ◽  
pp. 4181-4187
Author(s):  
P. L. N. Sowjanya
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Diabetic Retinopathy ◽  
Medical Imaging ◽  
Early Detection ◽  
Health Care System ◽  
Disease Diagnosis ◽  
Epidemiology Study ◽  
Speed Up ◽  
Care System

Diabetic retinopathy is one of the prevalent reasons of sight impairment in this day and age According to an epidemiology study, diabetic retinopathy affects one out of every three diabetics. In today's world, disease diagnosis is an essential part of medical imaging. In medical imaging, machine learning gives a greater vision for detecting disease. The objective is to detect diabetic retinopathy using ML. Machine learning in medical imaging could speed up and enhance the detection of sight caused by sugar. In order to detect diabetic retinopathy quickly and support the health-care system, this study will look at several machine learning methodologies, algorithms, and simulations. CNN is used to train the model.

Should Screening Programs Be Perfect?

PEDIATRICS ◽  
1983 ◽  
Vol 71 (2) ◽  
pp. 301-301
Author(s):  
PHILIP R. WYATT
Keyword(s):  
United States ◽  
Health Care ◽  
Early Detection ◽  
New England ◽  
Health Care System ◽  
The United States ◽  
Neonatal Hypothyroidism ◽  
Screening Programs ◽  
Care System

To the Editor.— The report of the New England Regional Screening Program1 on neonatal hypothyroidism is a stunning illustration of the vulnerability of screening programs. It is unfortunate that this experience will probably be used as an argument to minimize the input of screening programs in the health care system in the United States. The report illustrates that, in addition to the 2% of the screened population that eluded the program, 14 infants with hypothyroidism escaped the full benefits of early detection and treatment.

scholarly journals A Blockchain-Based Intelligent Machine Learning System for Smart Health Care

2021 ◽  
Author(s):  
Mir Hassan ◽  
Remigijus Paulavicius ◽  
Ernestas Filatovas ◽  
Adnan Iftekhar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care System ◽  
Patient Autonomy ◽  
Learning System ◽  
Security Level ◽  
Blockchain Technology ◽  
Smart Health ◽  
Intelligent Methods ◽  
Care System

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

scholarly journals Early detection and prevention of skin cancer are important part of the health care system

2010 ◽  
Vol 39 (6) ◽  
pp. 1-8
Author(s):  
Goran Videnović ◽  
Ilija Tripković ◽  
Dragan Krasić ◽  
Goran Bjelogrlić ◽  
Bojana Videnović
Keyword(s):  
Health Care ◽  
Skin Cancer ◽  
Early Detection ◽  
Health Care System ◽  
Early Detection And Prevention ◽  
Care System

scholarly journals Towards for Designing Intelligent Health Care System Based on Machine Learning

2021 ◽  
Vol 17 (2) ◽  
pp. 120-128
Author(s):  
Nada Noori ◽  
Ali Yassin
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Health Care ◽  
Health Care System ◽  
Support Vector ◽  
Care Providers ◽  
Number Of Patients ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Care System

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.

Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System

2018 ◽  
Vol 8 (2) ◽  
pp. 44-64
Author(s):  
Alvaro J Riascos ◽  
Natalia Serna
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Length Of Stay ◽  
Health Care System ◽  
Prevention Program ◽  
Health Care Systems ◽  
Cost Savings ◽  
Machine Learning Techniques ◽  
Patient Treatment ◽  
Care System

Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in which the decision of whether to intervene on a patient depends on a simple decision model and the prediction of the patient risk of an annual length-of-stay using machine learning techniques. These results show that the prevention program achieves significant cost savings relative to several base scenarios for program efficacies greater than or equal to 40% and intervention costs per patient of 100,000 to 700,000 Colombian pesos (i.e., approximately 14% to 100% of the average cost per patient in Colombia statuary health care system). This article also shows how tree-based methods outperform linear regressions when predicting an annual length-of-stay and the final model achieves a lower out-of-sample error compared to those of the Heritage Health Prize.

scholarly journals Application of a Mobile Chronic Disease Health-Care System for Hypertension Based on Big Data Platforms

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Dingkun Li ◽  
Hyun Woo Park ◽  
Erdenebileg Batbaatar ◽  
Lkhagvadorj Munkhdalai ◽  
Ibrahim Musa ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Chronic Disease ◽  
Health Care System ◽  
Disease Diagnosis ◽  
User Input ◽  
Step Procedure ◽  
Public Data ◽  
Patient Health ◽  
Care System

Hadoop is a globally famous framework for big data processing. Data mining (DM) is the key technique for the discovery of the useful information from massive datasets. In our work, we take advantage of both platforms to design a real-time and intelligent mobile health-care system for chronic disease detection based on IoT device data, government-provided public data and user input data. The purpose of our work is the provision of a practical assistant system for self-based patient health care, as well as the design of a complementary system for patient disease diagnosis. This system was only applied to hypertensive disease during the first research stage. Nevertheless, a detailed design, an implementation, a clear overview of the whole system, and a significant guide for further work are provided; the entire step-by-step procedure is depicted. The experiment results show a relatively high accuracy.

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