Details

Autor(en) / Beteiligte

• Misra, Praveen Kumar
• Devi, G. Meena
• Cruz, Yoni Magali Maita
• Cotrina-Aliaga, Juan Carlos
• Chatterjee, Pratik
• Gangodkar, Durgaprasad
• Kumaresan
• Shanmugam, Siva
• Rajagopal, Karthikeyan
• Vijayakumar, M. D.

Titel

Breast cancer detection based on PCA feature selection process using an ensemble learning algorithm

Ist Teil von

• AIP Conference Proceedings, 2024, Vol.2937 (1)

Ort / Verlag

Melville: American Institute of Physics

Erscheinungsjahr

2024

Beschreibungen/Notizen

• Breast Cancer (BC), more than any other malignancy, is one of the main reasons of mortality amongst women. Due to disease’s intricacy, varying patient population samples and changing treatment protocols, accurate diagnosis of breast cancer is extremely challenging. Better diagnostic procedures are critical for tailored care and treatment, as well as reducing and controlling cancer recurrence. This study investigates the early identification of breast cancer using a variety of ensemble methodologies. The dataset that has been utilized in this work is chosen to forecast breast cancer based on nine individual variables, including BMI, age, insulin, glucose and a homeostasis model evaluation. This research provides a suggested strategy based on feature selection that uses Principal Component Analysis (PCA) as well as the ensemble technique for dramatically lessening the dimensionality of features and enhance breast cancer classification. The suggested method is tested using the Wisconsin Breast Cancer Dataset, which is open to the public (WBCD dataset). This study focuses on evaluating and comparing the efficacy of several ensemble approaches based on PCA feature selection in early diagnosis of BC. LogitBoost is the most precise model amongst all the ensemble approaches, according to the findings. The LogitBoost algorithm beats the other ensemble methods, with an accuracy of 72.46 percent. The findings of data analysis show that ensemble classifiers outperform state-of-the-art approaches in detecting BC.