A Comparative Study on Prediction of Dengue Fever Using Machine Learning Algorithm

2020 ◽  
pp. 501-510
Author(s):  
Saif Mahmud Khan Dourjoy ◽  
Abu Mohammed Golam Rabbani Rafi ◽  
Zerin Nasrin Tumpa ◽  
Mohd. Saifuzzaman
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Dengue Fever ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals A Survey on Fatigue Detection of Workers Using Machine Learning

2020 ◽  
Vol 11 (3) ◽  
pp. 1-8
Author(s):  
Nisha Yadav ◽  
Kakoli Banerjee ◽  
Vikram Bali
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Human Performance ◽  
Learning Algorithm ◽  
Facial Features ◽  
Machine Learning Algorithm ◽  
Computer Screen ◽  
Health Risk Factor ◽  
Fatigue Detection

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm

2021 ◽  
pp. 21-30
Author(s):  
S. Magesh ◽  
V. R. Niveditha ◽  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
P. S. Rajakumar
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Comparative Study ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Pradeepa H. Dakappa ◽  
Keerthana Prasad ◽  
Sathish B. Rao ◽  
Ganaraja Bolumbu ◽  
Gopalkrishna K. Bhat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Dengue Fever ◽  
Bacterial Infections ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tympanic Temperature ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Temperature Recording

Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n=96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p<0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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