An Innovative Analysis of Time Series-Based Detection Models for Improved Cancer Detection in Modern Healthcare Environments
Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/114 |
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| Summary: | Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods such as blood tests, MRI scans, and gene expression profiling, it is now possible to quickly and accurately diagnose many types of cancer. Early detection of cancer can lead to improved outcomes for patients and can even help save lives. Time series analysis is a data mining technique used to identify and analyze the temporal patterns in datasets. The proposed model reached 91.30% accuracy, 90.11% precision, 92.46% recall, and a 90.12% F1-score. This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. Innovations in time series analysis for cancer detection can have a significant impact on modern healthcare. Time series analysis is a mathematical method used to analyze trends in data over multiple periods. It can be used to identify patterns that may indicate early signs of cancer. |
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| ISSN: | 2673-4591 |