Early Prediction of Childhood Obesity Using Machine Learning Techniques

2020 ◽  
pp. 1431-1440
Author(s):  
Kakali Chatterjee ◽  
Upendra Jha ◽  
Priya Kumari ◽  
Dhatri Chatterjee
Keyword(s):  
Machine Learning ◽  
Childhood Obesity ◽  
Machine Learning Techniques ◽  
Early Prediction ◽  
Learning Techniques

2: EARLY PREDICTION OF PATIENT DETERIORATION USING MACHINE LEARNING TECHNIQUES WITH TIME SERIES DATA

2016 ◽  
Vol 44 (12) ◽  
pp. 87-87 ◽  
Author(s):  
Sareen Shah ◽  
David Ledbetter ◽  
Melissa Aczon ◽  
Alysia Flynn ◽  
Sarah Rubin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Early Prediction ◽  
Patient Deterioration ◽  
Learning Techniques

scholarly journals Predict Diabetes Mellitus Using Machine Learning Algorithms

2021 ◽  
Vol 2089 (1) ◽  
pp. 012002
Author(s):  
Bano Farhana ◽  
K Munidhanalakshmi ◽  
Dr R. Madana Mohana
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Skin Thickness ◽  
Early Prediction ◽  
Type1 Diabetes ◽  
Learning Techniques ◽  
Type2 Diabetes

Abstract Diabetes mellitus has become a very frequent disease that affects totally different organs of human body. Diabetes cause diverge depending on genetic, family history, health and environmental factors. Diabetes mellitus refers to a gaggle of diseases that affect how your body uses blood glucose. The underlying reason behind diabetes varies by type. But, despite what kind of diabetes you’ve got, it will cause excess sugar in your blood. Diabetes will be of two types, they are Type1 Diabetes and Type2 Diabetes. Early prediction will help in society a lot. It will provides the humanlife in safe way. The aim of this analysis is to develop a system that predicts the diabetes with a better accuracy. Parameters used to predict the type of Diabetes Mellitus are Glucose, Pregnancies, skin thickness, Blood pressure, Insulin, BMI, Diabetes pedigree function, age and upshot. In this we are with different machine learning algorithms, namely SVM, ANN, Decision tree, Logistic regression and Farthest first to predict the accuracy. Our experimental results show that farthest first attain superior correctness compare to dissimilar machine learning techniques.

scholarly journals Early prediction of heart disease using the most significant features of diabetes by machine learning techniques

2021 ◽  
Vol 7 (1) ◽  
pp. 168-178
Author(s):  
Avijit Kumar Chaudhuri ◽  
Dr. Anirban Das ◽  
Dr. Deepankar Sinha ◽  
Dr. Dilip K. Banerjee
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Early Prediction ◽  
Learning Techniques
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