Comparative Analysis of Supervised Machine Learning Algorithms for GIS-Based Crop Selection Prediction Model

2019 ◽  
pp. 309-314 ◽  
Author(s):  
Preetam Tamsekar ◽  
Nilesh Deshmukh ◽  
Parag Bhalchandra ◽  
Govind Kulkarni ◽  
Kailas Hambarde ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Prediction Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Crop Selection

scholarly journals Non-Invasive Prediction Model to Detect Sepsis using Supervised Machine Learning Algorithms

2020 ◽  
Vol 8 (5S) ◽  
pp. 50-52
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Prediction Model ◽  
Tissue Damage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Blood Samples ◽  
Non Invasive ◽  
Life Threatening

Sepsis is a life-threatening disease that causes tissue damage, organ failure and results in the death of millions of people. Sepsis is one of the highest risky diseases identified globally. A large proportion of these deaths occur in developing countries due to inaccessibility of hospitals or lack of resources. Blood samples are taken to confirm sepsis, but it requires the presence of laboratory and is time-consuming. The aim and objective of this study is to develop a practical, non-invasive sepsis prediction model that can be used to detect sepsis using supervised machine Learning algorithms. For this retrospective analysis, we used the data available from Physio-Net database.

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