A New Range-based Breast Cancer Prediction Model Using the Bayes' Theorem and Ensemble Learning

Khozama, Sam; Mayya, Ali Mahmoud

doi:10.5755/j01.itc.51.4.31347

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Other applications have generated concise sets of meaningful summary features from expert-defined rules [ 50 ] and enriched EHR representation through the integration of knowledge graphs [ 51 ] and hierarchical code classifications [ 52 ]. Furthermore, known associations between diseases and their risk factors have been considered, either by weighting their contribution to the outcome [ 53 ] or through posterior regularisation [ 54 ]. Finally, rule-based classifiers formalising physicians’ knowledge have been combined with supervised learning algorithms by averaging ensemble [ 55 ] and voting ensemble [ 56 ].…”

Section: Previous Workmentioning

confidence: 99%

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi,

Bogliolo,

Montagna

2024

BMC Med Inform Decis Mak

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Background Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. Methods The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. Results The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. Conclusions By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

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Section: Previous Workmentioning

confidence: 99%

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi,

Bogliolo,

Montagna

2024

BMC Med Inform Decis Mak

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“…Obtaining the following results for the LR model 81.9%; GBT with 82%; RF with 82.8%, respectively. Similarly, in [25] they proposed a model to detect breast cancer using ML models. The tests were performed on a dataset consisting of 317,880 clinical observations.…”

Section: Previous Studiesmentioning

confidence: 99%

Breast Cancer Prediction using Machine Learning Models

Iparraguirre-Villanueva¹,

Epifanía-Huerta²,

Torres-Ceclén³

et al. 2023

IJACSA

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Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.

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“…By combining ensemble learning and Bayes theorem, Sam Khozama and Ali M. Mayya [2] developed a novel range-based breast cancer prediction model. The goal was to forecast BC with a range of 0% to 100%.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

Poornima,

Mamatha,

Krishnamai

et al. 2023

E3S Web Conf.

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Breast cancer is the most common cancer among women. It occurs when few breast cells begin to grow abnormally. The national average for 2022 is 100.4 cases per 1,00,000 people, with a large number of women being diagnosed with breast cancer. The objective is to design a prediction system that can predict breast cancer at early stages using a set of attributes that have been selected from a critical dataset. The Wisconsin Kaggle dataset is used for this experiment. The goal of this work is to predict breast cancer utilizing hybrid machine learning methodologies, such as SVM and PCA. ML algorithms that could help to predict cancer, as the early detection of this disease would help to slow down the progression of other diseases. In our paper, we are implementing Hybrid algorithms like PCA and SVM and optimizing SVM with k-fold cross-validation for predicting Breast cancer at early stages with high accuracy. The goal is to raise the fraction of early-stage breast cancer detection and to reduce mistake rates with maximum precision, which are sustainable.

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A New Range-based Breast Cancer Prediction Model Using the Bayes' Theorem and Ensemble Learning

Cited by 7 publications

References 26 publications

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Breast Cancer Prediction using Machine Learning Models

Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

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