International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
A SURVEY ON BLOOD DISEASE DETECTION USING MACHINE LEARNING Senthil Kumar V1, Ragul S S2, Ramya Shree3, Agustus A4 1
Assistant Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology [anonymous], Coimbatore, Tamilnadu, India 2-4 Department of Computer Science and Engineering, Kumaraguru College of Technology [anonymous], Coimbatore, Tamilnadu, India ---------------------------------------------------------------------------***-----------------------------------------------------------------------
Abstract— Technology is changing the world to live in easy way, but not all people in the world experiencing technology. To them this world is still giving more complex problems. We believe this proposal will make them to live easier. Now a days, people are spending lot of money in laboratories and in hospitals. In laboratories, they are using high level software which requires highly configured and specified PCs, hardware. Some laboratories in towns and village does not have PCS with high specification. This project will be used by entry level PCs. By running .py, the user will be taken to local host app UI and then one will promptly enter the required values to predict blood deficiency disease (required fields varies depends on diseases ) And if client want to view and use the project through online mode they can access the endpoints provided by Streamlit. And the machine learning model will classify the disease and show result with nutrition chart .Using machine learning with python, to create model and streamlit for User Interface. Keywords—Model selection, Random forest, Gaussian, SVC. 1. INTRODUCTION Machine learning has undergone vital development over the past decade and is already used with success in several bright applications covering a good array of knowledge connected issues. One in all the foremost interesting queries is whether or not it will be with success applied to the sphere of medical medicine and what reasonably knowledge is required. Laboratory tests area unit familiar ensure, exclude, classify or monitor diseases and to guide treatment. However, verity power of laboratory check results is often underestimated, since clinical laboratories tend to report check results as individual numerical or categorical values, with physicians concentrating primarily on those values that fall outside a given reference vary.
2. PROJECT OBJECTIVE The objective of the project is to classify and predict diseases using machine learning algorithms with the help of blood sample results provided by the laboratory. The most important questions is how this technologies and software can be applied to medical field successfully.Blood data analysis, only machine learning classification algorithms were used. They were as follows: Gaussian Random forest. Support vector classification (SVC).
A. Gaussian classification: Gaussian Processes square measure a generalization of the mathematician chance distribution and may be used because the basis for stylish non-parametric machine learning algorithms for classification and regression. They’re a kind of kernel model, like SVMs, and in contrast to SVMs, they're capable of predicting extremely tag category membership chances, though the selection and configuration of the kernel used at the center of the strategy will be difficult. Since mathematician works well for
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