Disease prediction based on micro array classification using deep learning techniques

2020 ◽  
Vol 77 ◽  
pp. 103189 ◽  
Author(s):  
V. Chandrasekar ◽  
V. Sureshkumar ◽  
T. Satish Kumar ◽  
S. Shanmugapriya
Keyword(s):  
Deep Learning ◽  
Disease Prediction ◽  
Learning Techniques ◽  
Micro Array

scholarly journals Cardiovascular disease prediction using deep learning techniques

2020 ◽  
Vol 981 ◽  
pp. 022006
Author(s):  
Syed Nawaz Pasha ◽  
Dadi Ramesh ◽  
Sallauddin Mohmmad ◽  
A. Harshavardhan ◽  
Shabana
Keyword(s):  
Cardiovascular Disease ◽  
Deep Learning ◽  
Disease Prediction ◽  
Learning Techniques

Disease prediction and diagnosis system in cloud based IoT: A review on deep learning techniques

2020 ◽  
Author(s):  
K. CH Suneetha ◽  
R. Shanmuka Shalini ◽  
Vijaya Kumar Vadladi ◽  
Machunoori Mounica
Keyword(s):  
Deep Learning ◽  
Disease Prediction ◽  
Diagnosis System ◽  
Learning Techniques

Tomato And Potato Leaf Disease Prediction With Health Benefits Using Deep Learning Techniques

2021 ◽  
Author(s):  
K. Karthik ◽  
S. Rajaprakash ◽  
S. Nazeeb Ahmed ◽  
Rishan Perincheeri ◽  
C. Risho Alexander
Keyword(s):  
Deep Learning ◽  
Health Benefits ◽  
Disease Prediction ◽  
Potato Leaf ◽  
Leaf Disease ◽  
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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