Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63573658" target="_blank" >RIV/70883521:28140/23:63573658 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICETAS59148.2023.10346582" target="_blank" >http://dx.doi.org/10.1109/ICETAS59148.2023.10346582</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICETAS59148.2023.10346582" target="_blank" >10.1109/ICETAS59148.2023.10346582</a>
Alternative languages
Result language
angličtina
Original language name
Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis
Original language description
When it comes to cancer-related mortality, breast cancer is the most common and predominant kind in women; lung cancer is the most common. It ranks second overall. Global scientists have been working very hard to fight this illness for a long time. Furthermore, significant advancements have been achieved in the fields of machine learning and data mining for the extraction and synthesis of insightful knowledge, even from extremely complicated data sources. Machine learning models may carry out a number of functions, including clustering, classification, and prediction, by using the knowledge obtained from data. In this study, we examine the relationship between a dataset's several features and a diagnosis of breast cancer. We use five different variables extracted from X-ray images in the dataset to predict the existence of breast cancer using a supervised learning classification technique called Random Forest. The open-source web repository Kaggle served as the source of this dataset. We use Principal Component Analysis (PCA), a dimensionality reduction method, on the data prior to putting the Machine Learning algorithm into practice. We then assess the performance of the Machine Learning algorithm using measures like accuracy, precision, recall, F1-score, and support. Additionally, we compare the model's output with Random Forest and examine the performance metrics it produces with and without PCA analysis
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Engineering Technologies and Applied Sciences: Shaping the Future of Technology through Smart Computing and Engineering, ICETAS 2023
ISBN
979-8-3503-2710-6
ISSN
2769-450X
e-ISSN
2769-4518
Number of pages
7
Pages from-to
1-7
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Piscataway, New Jersey
Event location
Bahrain
Event date
Oct 25, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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