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 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 Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

2016 ◽  
Vol 6 (6) ◽  
pp. 2674
Author(s):  
Nimisha Singh ◽  
Rana Gill
Keyword(s):  
Machine Learning ◽  
Retinal Disease ◽  
Learning Approach ◽  
Learning Approaches ◽  
Retinal Area ◽  
Original Image ◽  
Feature Generation ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Image Pattern

<p class="Abstract">Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to detect the true retinal area. Artefacts like eyelids and eyelashes are come along with retinal part so removal of artefacts is the big task for better diagnosis of disease into the retinal part.  In this paper, we have proposed the segmentation and use machine learning approaches to detect the true retinal part. Preprocessing is done on the original image using Gamma Normalization which helps to enhance the image  that can gives detail information about the image. Then the segmentation is performed on the Gamma Normalized image by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Superpixel is used to reduce the complexity of image processing task and provide suitable primitive image pattern. Then feature generation must be done and machine learning approach helps to extract true retinal area. The experimental evaluation gives the better result with accuracy of 96%.</p>

scholarly journals A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

2021 ◽  
Vol 67 (2) ◽  
pp. 2123-2139
Author(s):  
Muhammad Kashif ◽  
Ayyaz Hussain ◽  
Asim Munir ◽  
Abdul Basit Siddiqui ◽  
AaqifAfzaal Abbasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring Systems ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Healthcare Monitoring

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.

scholarly journals An Efficient Technique for Disease Prediction by Using Enhanced Machine Learning Algorithms for Categorical Medical Dataset

2021 ◽  
Vol 50 (1) ◽  
pp. 102-122
Author(s):  
Veera Anusuya ◽  
V Gomathi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Large Datasets ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Disease Prediction ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Prediction Mechanism ◽  
Medical Dataset

In the 20th century, it is evident that there is a massive evolution of chronic diseases. The data mining approaches beneficial in making some medicinal decisions for curing diseases. But medical data may consist of a large number of data, which makes the prediction process a very difficult one. Also, in the medical field, the dataset may involve both the small database and extensive database. This creates the study of a complex one for disease prediction mechanism. Hence, in this paper, we intend to use a practical machine learning approach for disease prediction of both large and small datasets. Among the various machine learning procedures, classification, and clusters method play a significant role. Therefore, we introduced the enhanced classification and clusters approach in this work for obtaining better accuracy results for disease prediction. In this proposed method, a process of preprocessing is involved, followed by Eigen vector extraction, feature selection, and classification Further, the most suitable features are selected with the use of Multi-Objective based Ant Colony Optimization (MO-ACO) from the extracted features for increasing the classification and clusters. Here we have shown the novelty in every stage of the implementation, such as feature selection, feature extraction, and the final prediction stage. The proposed method will be compared with the existing technique on the measure of precision, NMI, execution time, recall, and Accuracy. Here we conclude with the solution having more accuracy for both small and large datasets.

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