Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Ebrahim, Mohamed Wahba; Sedky, Ahmed Ahmed Hesham; Mesbah, Saleh

doi:10.3390/data8020035

Cited by 26 publications

(11 citation statements)

References 28 publications

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“…Similar use for prediction accuracy [14] in assessing best ML technique for breast cancer prediction recorded an accuracy score of 98.7% for techniques such as decision trees and other ensemble techniques. ML principles and applications in real world systems have also been explored [15].…”

Section: Balanced Accuracy Process Diagrammentioning

confidence: 97%

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Owusu-Adjei

Hayfron-Acquah

Twum

et al. 2023

Preprint

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Focus on predictive algorithm and its performance evaluation is extensively covered in most research studies. Best predictive models offer Optimum prediction solutions in the form of prediction accuracy scores, precision, recall etc. Prediction accuracy score from performance evaluation have been used as a determining factor for appropriate model recommendations use. It is one of the most widely used metric for identifying optimal prediction solutions irrespective of context or nature of dataset, size and output class distributions between the minority and majority variables. The key research question however is the impact of using prediction accuracy as compared to balanced accuracy in the determination of model performance in healthcare and other real-world application systems. Answering this question requires an appraisal of current state of knowledge in both prediction accuracy and balanced accuracy use in real-world applications including a search for related works that highlight appropriate machine learning methodologies and techniques.

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Section: Balanced Accuracy Process Diagrammentioning

confidence: 97%

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Owusu-Adjei

Hayfron-Acquah

Twum

et al. 2023

Preprint

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show abstract

“…These methods are useful for risk assessment, patient health modeling, and policy gradient reinforcement learning [18][19] [20]. Combining survival analysis with deep networks improves time-to-event forecasting for hospital readmissions [19] [21]. Longitudinal patient trajectories and synthetic data augmentation improve hospitalization risk prediction.…”

Section: B Related Workmentioning

confidence: 99%

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Al-Atroshi,

Adnan Mohsin Abdulazeez

2024

ijcs

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Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

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“…The nonlinear case involves nonlinear relationships among predictors to achieve separation. Despite the conceptual superiority of non-linear algorithms and the often better predictive performance, they are more complex and therefore often hinder the ability to interpret the final model [84,85]. Explainability in the context of AI applications refers to our ability to explain why and under which circumstances a decision is made by a trained model.…”

Section: Modeling and Classificationmentioning

confidence: 99%

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Gallos,

Tryfonopoulos,

Shani

et al. 2023

Diagnostics

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Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

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Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Cited by 26 publications

References 28 publications

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

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