Review on machine learning based disease diagnostics and classifications model development through a big data frameworks
There is a growing demand for real-time analysis and processing of the massive amounts of unstructured or semi-structured data being produced by the healthcare industry today. To better investigate the most useful information and give better medical treatment, modern medicine requires an interactive...
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| creator | Sahare, Sneha A. Gote, Ashwini Ingole, Kartik |
| description | There is a growing demand for real-time analysis and processing of the massive amounts of unstructured or semi-structured data being produced by the healthcare industry today. To better investigate the most useful information and give better medical treatment, modern medicine requires an interactive and intelligent System capable of handling a big biological dataset with human-computer interaction. The impact of Machine Learning (ML) and big data on medicine, however, is presently difficult to conceptualise. The primary objective of this study is to create a Bigdata framework for the detection and categorization of diseases via the use of machine learning. This study applies big data analysis to the medical field, focusing on the diagnosis of three conditions: chronic kidney disease (CKD), heart disease (HD), and diabetes. |
| doi_str_mv | 10.1063/5.0241705 |
| format | Conference Proceeding |
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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2024, Vol.3188 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_3142698461 |
| source | AIP Journals Complete |
| subjects | Big Data Data analysis Demand analysis Health services Heart diseases Industrial development Interactive systems Kidney diseases Machine learning Real time Structured data Unstructured data |
| title | Review on machine learning based disease diagnostics and classifications model development through a big data frameworks |
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