scholarly journals Disease Prediction Using Machine Learning Over Big Data

2018 ◽  
Vol 8 (1) ◽  
pp. 01-08 ◽  
Author(s):  
Vinitha S ◽  
Sweetlin S ◽  
Vinusha H ◽  
Sajini S
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Disease Prediction

scholarly journals Diabetes Impacted Cardiovascular Disease Prediction using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4376-4378
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Heart Diseases ◽  
Medical Data ◽  
Structured Data ◽  
Disease Prediction ◽  
Health Communities ◽  
Prediction Systems

Utilizing big data growth in biological and health communities, an accurate analogy of medical data can benefit the detection of diabetes impacting cardiovascular diseases. Using k-Means clustering (kMC) algorithm for structured data of heart disease patients, we narrow down to cardiovascular diseases impacted by diabetes. To our knowledge, none of the previous work focused on predicting heart diseases specifically for diabetes patients. Contrasted to multiple other prediction algorithms, the accuracy of predicting in our proposed algorithm is faster than that of other prediction systems for cardiovascular diseases.

scholarly journals Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 8869-8879 ◽  
Author(s):  
Min Chen ◽  
Yixue Hao ◽  
Kai Hwang ◽  
Lu Wang ◽  
Lin Wang
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Disease Prediction

scholarly journals Disease Prediction Using Machine Learning Over Big Data

2018 ◽  
Author(s):  
Vinitha S ◽  
Sweetlin S ◽  
Vinusha H ◽  
Sajini S
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Disease Prediction

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.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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