scholarly journals A Machine Learning Approach to Low-Value Health Care: Wasted Tests, Missed Heart Attacks and Mis-Predictions

2019 ◽  
Author(s):  
Sendhil Mullainathan ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Learning Approach ◽  
Heart Attacks ◽  
Machine Learning Approach

scholarly journals Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

10.2196/21753 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e21753
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Shiyng-Yu Lin ◽  
Jenny L Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Preventive Medicine ◽  
Learning Approach ◽  
Care Assessment ◽  
Machine Learning Approach

scholarly journals Review on Smart Health Care Monitoring Systems

2021 ◽  
pp. 418-422
Author(s):  
Vasishth V. Katre ◽  
Dr. P. N. Chatur
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health System ◽  
Monitoring Systems ◽  
Learning Approach ◽  
Health Issues ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Smart Health ◽  
Health Care Monitoring

Document IoT is leading in smart health care system. Using different sensors it's possible to monitor the patients healthcare remotely. This is unimagined and leads to a spatial longitude amalgamated with machine learning approach. Leading to smart health care, and headway in medical field. It may lead to know severe health issues ahead of time which would be tranquil to the health system. Which would benefit the hospital administration and management. This paper elucidates on the distinct sort of IoT based health care monitoring systems. The aim is to juxtapose the present health care IoT systems.

scholarly journals Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 90
Author(s):  
Bartosz Ćwiklinski ◽  
Agata Giełczyk ◽  
Michał Choraś
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Team Building ◽  
Learning Approach ◽  
Professional Football ◽  
Football Team ◽  
Machine Learning Approach ◽  
Transfer Success ◽  
Professional Tool ◽  
Successful Transfer

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

scholarly journals Machine Learning Approach for Classification of Cancer Stages

2019 ◽  
pp. 1-4
Author(s):  
Shubham Hingmire
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Health Care ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Classification Methods ◽  
Medical Diagnoses ◽  
Machine Learning Approach ◽  
Computer Aided

The simplest form of health care is diagnosis and prevention. of disease. Machine learning (ML) methods help achieve this goal. This project aims to compare method of computer aided medical diagnoses. The ?rst of these methods is a classify disease diagnosis according to their data. This involves the training of an Arti?cial Neural Network to respond to several patient parameters. And also comparing various classification methods the purpose research classifier classi?es the patients in two class ?rst is malignant and second is benign.

Classifying lung cancer stage from health care claims with a clinical algorithm or a machine-learning approach.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 6589-6589
Author(s):  
Gabriel A. Brooks ◽  
Nancy Lynn Keating ◽  
Savannah L Bergquist ◽  
Mary Beth Landrum ◽  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Health Care ◽  
Learning Approach ◽  
Cancer Stage ◽  
Clinical Algorithm ◽  
Machine Learning Approach ◽  
Health Care Claims
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