Prediction of Diabetes Using Machine Learning Algorithms in Healthcare
Also Available Domains Machine Learning
Abstract
There are several machine learning techniques that are used to perform predictive analytics over big data in various fields. Predictive analytics in healthcare is a challenging task but ultimately can help practitioners make big data-informed timely decisions about patient’s health and treatment. This paper discusses the predictive analytics in healthcare, six different machine learning algorithms are used in this research work. For experiment purpose, a data set of patient’s medical record is obtained and six different machine learning algorithms are applied on the data set. Performance and accuracy of the applied algorithms is discussed and compared. Comparison of the different machine learning techniques used in this study reveals which algorithm is best suited for prediction of diabetes. This paper aims to help doctors and practitioners in early prediction of diabetes using machine learning techniques.
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Scripts : JavaScript, Jquery.
Server side Script : Python
Framework : Django, Flask
Learning Outcomes
· Scope of Real Time Application Scenarios
· Objective of the project
· How Internet Works
· What is a search engine and how browser can work?
· What type of technology versions are used?
· Use of HTML,and CSS on UI Designs
· Data Parsing Front-End to Back-End
· Working Procedure
· Introduction to basic technologies used for
· How project works.
· Input and Output modules
· Practical exposure to
· Frame work use
· Datasets properties
· Deep learning algorithms.
· What is sentiment analysis
· Data preprocessing techniques
· What is word embedding models
· What is Ada boost and Gradient boosting
· How random forest algorithm will work
· What is voting system
o Hardware and software tools.
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o Work on Creative ideas
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