Prediction of diabetic patients using various machine learning techniques

2021 ◽  
Vol 66 (2) ◽  
pp. 100
Author(s):  
Manpreet Kaur ◽  
Shalli Rani ◽  
Deepali Gupta ◽  
Amit Kumar Manocha
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Techniques

Prediction of diabetic patients using various machine learning techniques

2021 ◽  
Vol 66 (2) ◽  
pp. 100
Author(s):  
Amit Kumar Manocha ◽  
Deepali Gupta ◽  
Shalli Rani ◽  
Manpreet Kaur
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Techniques

scholarly journals A Classification System for Diabetic Patients with Machine Learning Techniques

2019 ◽  
Vol 4 (3) ◽  
pp. 729-744 ◽  
Author(s):  
Vandana Rawat ◽  
Suryakant
Keyword(s):  
Machine Learning ◽  
Public Health Care ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Insulin Production ◽  
Pima Indians ◽  
Data Set ◽  
Learning Techniques ◽  
Significant Public Health

Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.

scholarly journals Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 546
Author(s):  
Omer Mujahid ◽  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Machine Learning ◽  
Diabetic Patient ◽  
Life Quality ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Approaches ◽  
Prediction Horizon ◽  
Learning Techniques

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

scholarly journals Type II Diabetes Prediction using Combo of SVM

2019 ◽  
Vol 8 (6) ◽  
pp. 712-715
Keyword(s):  
Machine Learning ◽  
Type Ii Diabetes ◽  
Human Life ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Techniques ◽  
Healthcare It ◽  
Diabetes Prediction ◽  
Almost All ◽  
Two Sides

In 21th century, IT plays a very important and helpful role in health care industries acting as a savior to human life. Data mining and machine learning are two sides of healthcare-IT. Proposed system considers one of the most common chronic diseases called diabetes. India and almost all other countries are worried about diabetic patients, so diabetes can termed as a global chronic disease. In this paper, well-known predictive machine learning techniques viz. SVM, Random Tree and ANN are applied on PIMA dataset. Results of SVM, ANN, and RT are 90.1%, 88.02%, and 83.59% respectively

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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