A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Omotehinwa, Temidayo Oluwatosin; Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga

doi:10.1016/j.health.2023.100218

Cited by 15 publications

(4 citation statements)

References 37 publications

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“…Better patient outcomes and physician judgement are promised by the model's enhanced capabilities(A. Sharma et al, 2024).The TPE-optimized Borderline-SMOTE Light GBM model performs exceptionally well as compared to previous studies (Omotehinwa et al, 2023). The Voting classifier fared better than the other classifiers, as seen by its high accuracy of 98.77%.…”

Section: Introductionmentioning

confidence: 92%

Machine Learning Techniques for Prediction and Classification of Breast Cancer

Pattnaik,

Naushad,

Pattanaik

et al. 2024

Proceedings of Twenty First AIMS International Conference on Management

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Breast cancer continues to be a menace in the healthcare industry, grappling not only women but also a significant number of males. Every form of cancer begins with a tumour and can be classified into two types like malignant and benign. It is the malignant that turn out to be cancerous, growing abnormally. This study aimed at predicting and classifying the tumours into the said two groups. Related to this, machine learning techniques like decision tree and linear discriminant analysis are applied to identify 6 risk factors out of 30 different variables. The outcomes of this study are backed by substantial significance.

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Section: Introductionmentioning

confidence: 92%

Machine Learning Techniques for Prediction and Classification of Breast Cancer

Pattnaik,

Naushad,

Pattanaik

et al. 2024

Proceedings of Twenty First AIMS International Conference on Management

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“…This technique is operationalized through frameworks such as LightGBM and XGBoost. LightGBM, a gradient boosting framework, relies on the light-gradient boosting machine algorithm and has demonstrated its utility across various fields, including the diagnosis of breast cancer [49]. Conversely, XGBoost is a sophisticated library designed to enhance scalability and performance in a distributed environment.…”

Section: Supervised Learning Approachmentioning

confidence: 99%

Adaptive Hybrid Learning for Websites Vulnerability prediction

Hossain Hadi,

Hashim Al-Saedi

2024

J. Al-Qadisiyah Comp. Sci. Math.

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This study presents a comprehensive exploration of machine learning (ML) techniques for predicting vulnerabilities in websites, which is a critical aspect of modern cybersecurity. With the advancement of digital threats and the complexity of cyber-attacks, conventional security strategies have become increasingly inadequate. By employing machine learning algorithms such as Random Forest and Gradient Boosting, this study formulates models adept at identifying potential vulnerabilities within the website code. This approach responds to the escalating demand for enhanced security measures, in the face of increasingly sophisticated digital threats. By integrating anomaly detection findings through the Isolation Forest algorithm, this study enriches the training dataset, enabling models to adapt to both known and emerging vulnerability patterns The Gradient Boosting model slightly outperformed the Random Forest model in terms of overall accuracy, achieving a precision of 97% for the non-vulnerability class, and the vulnerability class had a precision of 90%, leading to an overall accuracy of 96.25%., which is attributed to its ability to iteratively learn from previous errors, thereby enhancing its adaptability to new vulnerabilities. This study underscores the significant potential of ML to enhance cybersecurity measures against website vulnerabilities.

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“…Temidayo Oluwatosin Omotehinwa, David Opeoluwa Oyewola, Emmanuel Gbenga Dada [18] focuses on breast cancer diagnosis with tree structured Parzen Estimator .…”

Section: Literature Surveymentioning

confidence: 99%

Novel Approach with Deep Learning Models For Melanoma Skin Cancer Detection

Patil,

Gawade,

Chavan

et al. 2024

AJCT

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Artificial intelligence methodologies with deep learning models proved very effective in many areas. This research focuses on using deep learning models for the detection of skin cancer melanoma in patients at an early stage. The growth of a malignant melanocytic tumor is the primary cause of melanoma, a deadly form of skin cancer. The most dangerous type of cancer, known as melanoma, is brought on by melanocytes, which produce pigment. Seventy-five percent of skin cancer fatalities are caused by melanoma. Only 15% of patients who have survived over the previous five years, according to a review of the survival rate, have received chronic treatment. The manual system is the issue. The mole has a maximum of six colors, thus the image is troublesome since it has a variety of tints and is hard for humans to distinguish. As a result, we created a model utilizing flask and a pre-trained deeplearning model to handle this issue. The suggested model is optimized using the SGD, RMSprop, Adam, Agagrad, Adadelta, and Nadam optimizers. According to testing, the suggested CNN model with the Adam optimizer performs the best at categorizing the dataset of skin cancer lesions. Also it gives the result specifically to the seven types of the skin cancer with their possible chances in percentage. It achieved a training accuracy of 99.73 % and a testing accuracy of 96.53, which is better than the previous results. In order to lower the mortality rate, this research intends to identify an early treatment for people with skin cancer.

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A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Cited by 15 publications

References 37 publications

Machine Learning Techniques for Prediction and Classification of Breast Cancer

Machine Learning Techniques for Prediction and Classification of Breast Cancer

Adaptive Hybrid Learning for Websites Vulnerability prediction

Novel Approach with Deep Learning Models For Melanoma Skin Cancer Detection

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