Use Case—Fraud Detection Using Machine Learning Techniques

Enzinger, Philipp; Li, Sangmeng

doi:10.1007/978-3-030-78829-2_3

Cited by 3 publications

(1 citation statement)

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“…The review highlights the growing trend of employing CNNs for feature extraction and classification tasks in healthcare [33], [34] while acknowledging the advancements in traditional ML algorithms like Random Forest and XGBoost, which still demonstrate excellence in certain diagnostic tasks [35], [36]. Despite the progress, a gap remains in the ease of clinical application and accessibility of these advanced ML techniques, with traditional models facing challenges due to their complexity and limitations in handling modern datasets [37], [38] The literature suggests the need for further research in enhancing the interpretability and clinical integration of DL models, as well as in exploring the potential of ML in non-invasive prognostic modeling and diagnosis using a variety of medical data sources [5], [30]. This gap indicates a direction for future research to focus on improving the applicability of DL models in clinical settings and on developing algorithms that can provide more accessible and interpretable diagnostic tools for cirrhosis and other liver diseases.…”

Section: Introductionmentioning

confidence: 99%

Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks

Endah H,

Wijaya,

Khotibul Ahsan

2023

J. Soft Comput. Explor.

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Cirrhosis, a prevalent and life-threatening liver condition, demands early detection for effective intervention. This study investigates the potential of machine learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in cirrhosis prediction using a dataset from Kaggle containing 418 observations and 20 attributes. Performance evaluation involves metrics like accuracy, precision, recall, and F1-score, revealing CNN's superior performance with an 84% accuracy rate. The study highlights the importance of algorithm selection and feature engineering in medical diagnosis. Moreover, a comparison with traditional machine learning techniques underscores CNN's prowess in this domain. Beyond cirrhosis, CNNs offer promise for automating feature extraction from medical imagery and recognizing complex patterns, potentially transforming diagnostic accuracy in healthcare.

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