P30. Development of a machine learning algorithm for prediction of complications and readmission after lumbar spinal fusion

2021 ◽  
Vol 21 (9) ◽  
pp. S154-S155
Author(s):  
Akash A. Shah ◽  
Sai Devana ◽  
Changhee Lee ◽  
Amador Bugarin ◽  
Alexander Upfill-Brown ◽  
...  
Author(s):  
Akash A. Shah ◽  
Sai K. Devana ◽  
Changhee Lee ◽  
Amador Bugarin ◽  
Elizabeth L. Lord ◽  
...  

2020 ◽  
Vol 20 (3) ◽  
pp. 329-336 ◽  
Author(s):  
Jaret M. Karnuta ◽  
Joshua L. Golubovsky ◽  
Heather S. Haeberle ◽  
Prashant V. Rajan ◽  
Sergio M. Navarro ◽  
...  

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

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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