Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis

Popis výsledku v původním jazyce

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&apos;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&apos;s output with Random Forest and examine the performance metrics it produces with and without PCA analysis

Název v anglickém jazyce

Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis

Popis výsledku anglicky

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&apos;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&apos;s output with Random Forest and examine the performance metrics it produces with and without PCA analysis

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, New Jersey

Místo konání akce

Bahrain

Datum konání akce

25. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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