Vector-Borne Disease Outbreak Prediction Using Machine Learning Techniques

2021 ◽  
pp. 227-241
Author(s):  
Sandali Raizada ◽  
Shuchi Mala ◽  
Achyut Shankar
Keyword(s):  
Machine Learning ◽  
Disease Outbreak ◽  
Machine Learning Techniques ◽  
Vector Borne Disease ◽  
Learning Techniques ◽  
Vector Borne

scholarly journals COVID 19 DISEASE PREDICTION VIA MACHINE LEARNING

2021 ◽  
Author(s):  
Dhrumil Gala
Keyword(s):  
Machine Learning ◽  
Virus Disease ◽  
Disease Outbreak ◽  
Great Accuracy ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques ◽  
Physical Attributes ◽  
Web App ◽  
True Colors

<p>This research focuses primarily on a new worldwide problem: the continuing corona virus disease outbreak [COVID-19]. The disease originated from China and slowly it got spread to different places in the world and started showing its true colors .Reportedly it was known to have caused from bats. While some symptoms are severe, for a big portion of the population, symptoms are minor, and as a result, people may be unaware that they are infected with COVID-19 and hence fail to visit and be diagnosed by a doctor[1].Thus this paper mainly focuses on detecting whether a person is suffering from this disease or not. I have made a web app which will take in your current physical attributes such as your body temperature and other physical attributes and the web app will predict whether you are suffering from this disease or not. I have used machine learning techniques such as linear regression to predict whether the person suffers from the disease or not. This web app has a great accuracy and it will predict the outcome with precision and thus is a very helpful app.<b></b></p>

scholarly journals COVID 19 DISEASE PREDICTION VIA MACHINE LEARNING

2021 ◽  
Author(s):  
Dhrumil Gala
Keyword(s):  
Machine Learning ◽  
Virus Disease ◽  
Disease Outbreak ◽  
Great Accuracy ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques ◽  
Physical Attributes ◽  
Web App ◽  
True Colors

<p>This research focuses primarily on a new worldwide problem: the continuing corona virus disease outbreak [COVID-19]. The disease originated from China and slowly it got spread to different places in the world and started showing its true colors .Reportedly it was known to have caused from bats. While some symptoms are severe, for a big portion of the population, symptoms are minor, and as a result, people may be unaware that they are infected with COVID-19 and hence fail to visit and be diagnosed by a doctor[1].Thus this paper mainly focuses on detecting whether a person is suffering from this disease or not. I have made a web app which will take in your current physical attributes such as your body temperature and other physical attributes and the web app will predict whether you are suffering from this disease or not. I have used machine learning techniques such as linear regression to predict whether the person suffers from the disease or not. This web app has a great accuracy and it will predict the outcome with precision and thus is a very helpful app.<b></b></p>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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