Improving diagnostic accuracy for breast cancer using prediction-based approaches

Bhangu, Kamalpreet Singh; Sandhu, Jasminder Kaur; Sapra, Luxmi

doi:10.1109/pdgc50313.2020.9315815

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…Chosen risk factors for the development of an improved diagnostic system Cancer Decision Tree [120], [197], [198] Spliter: best, max_depth: none, criterion: gini Handling a combination of numerical and categorical data, often encountered in medical data Random Forest [115], [120], [128], [197], [198] Criterion:…”

Section: Flexible In Tuning Parametersmentioning

confidence: 99%

“…gini, max_depth: none, n_estimators: 150 Effectively addressing class imbalance through techniques such as weighted classes AdaBoost [? ], [120] n_estimators: 50, learning_rate: 0.5, algorithm: SAMME Addressing the issue of class imbalance by assigning greater weight to prediction errors on samples from the positive class XGBoost [113], [115], [120], [128], [196], [197] n_estimators: 300, scale_pos_weight: 1, max_depth: 4, Sampling method: uniform, eta: 0.3, booster: gbtree Provides numerous parameters that can be optimized Bagging [125] n_estimators: 20, base_estimator: decision tree Becoming more robust to outliers SVM [7], [40], [108], [120], [160], [172] Kernel: rbf, gamma: 1, C: 10 Effective in high-dimensional feature space KNN [76], [108], [120], [124], [145], [199] Algorithm: ball_tree, p:2, n_neighbors: 14 Capturing non-linear and complex patterns in data MLP [115] Optimizer: Adam, learning rate: 0.001 Identifying complex patterns that may be associated with cancer CNN [96] Learning rate: 0.01, Epochs: 100, Optimizer: Adam…”

Section: Flexible In Tuning Parametersmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: Flexible In Tuning Parametersmentioning

confidence: 99%

Section: Flexible In Tuning Parametersmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…Early breast cancer is commonly identified by mammography, ultrasound, and so on. However, breast cancer still nearly 30% of cases are detected in the late stage of breast cancer [5]. We can improve the success rate and reduce the mortality rate if we find breast cancer early [6][7].…”

mentioning

confidence: 99%

Research on SPDTRS-PNN Based Intelligent Assistant Diagnosis for Breast Cancer

Zhou

Kong

Bian

et al. 2021

Preprint

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Background:Breast cancer is the second dangerous cancer in the world. How to identify breast cancer quickly and accurately is of great help to the treatment of breast cancer. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time-consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. Methods:This paper proposes the single-parameter decision-theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We structure fifteen models by combining five dimensionality reduction algorithms with three classification algorithms. We compared the accuracy and test time of fifteen models under different parameters or dimensions. We find that when the parameter value of SPDTRS is 2.5, the classification effect of SPDTRS combined with PNN is better. At this point, the number of 30 attributes of the original breast cancer data dropped to 12. Then the SPDTRS-PNN model is further optimized. We compared the accuracy and test time of the model under different SPREAD values in PNN, and established a better SPDTRS-PNN model.Result:We find the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093s.Conclusion:The experimental results show that the SPDTRS-PNN model can improve the accuracy of breast cancer recognition, reduce the time required for diagnosis, and achieve rapid and accurate breast cancer diagnosis.

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