Early Prediction of Chronic Kidney Disease Using Data Mining Techniques

Shad, Hasin Shahed; Jamal, Zeeshan; Ahmed, Salim; Momen, Sifat; Mansoor, Nafees

doi:10.1007/978-3-030-90321-3_79

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Data sets used in the literature might vary in terms of their size and types of variables. Collecting the related data is a time-consuming activity and a higher number of features would not necessarily lead to a higher accuracy in diagnosis [41,42]. For this reason, in many of the breast cancer diagnosis studies or similar applied health-related studies, feature selection is an important part of the methodology.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Breast Cancer Using Machine Learning Techniques

et al. 2022

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Breast cancer affects the majority of women around the world. Females are more likely to die as a result of this condition. By employing a variety of cutting-edge procedures, the samples are collected and the main cause of breast cancer is sought. The most modern techniques are logistic regression discriminant analysis and principal component analysis, both of which are useful in determining the causes of breast cancer. The Breast Cancer Wisconsin Diagnostic Dataset collects information via the Machine Learning Repository approach. As a result of the data correlation matrix processing, we are able to positively root our job. Principal component analysis, discriminant analysis, and logistic regression are utilized to extract the features. Models like Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machines, and Artificial Neural Networks are utilized and their performance is rigorously examined. The results suggest that the proposed strategy works effectively and reduces training time. These new methods help doctors understand the origins of breast cancer and distinguish between tumor kinds. Data mining techniques are used extensively, especially for feature selection. Conclusion: Among all models, the hybrid discriminant-logistic (DA-LR) feature selection model outperforms SVM and Naive Bayes. Keywords: breast cancer, naïve Bayes, neural network, machine learning, medical imaging support, vector machine Copyright (c) The Authors

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Section: Discussionmentioning

confidence: 99%

Prediction of Breast Cancer Using Machine Learning Techniques

et al. 2022

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“…Methods incorporating deep learning have improved the detection of kidney disease predictions. Group of authors in [12] presented their research on using five ML models, NB, SVM, Decision Tree (DT), LR and ANN with normalized features and obtained accuracy of 99.3% using DT in taken original dataset with all features.…”

Section: Related Workmentioning

confidence: 99%

Predicting Chronic Kidney Disease using ML algorithms and XAIV

2023

MCET

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Kidney disease is a significant health concern that is currently affecting individuals of all age groups. To predict the occurrence of chronic renal disease, a large number of scholars have employed machine learning and deep learning techniques. However, the efficacy of these methods is often hampered by a lack of transparency, which is a major issue in the application of artificial intelligence in healthcare and medical analysis. As such, the lack of clarity has prompted concern. To interpret the results of predictive models, the present study proposes the deployment of four machine learning algorithms, including Decision Tree, Logistic Regression, Multi-layer Perceptron Classifier, and Support Vector Machine, in combination with explainable AI (XAI) interface, leveraging the local interpretable model-agnostic explanation (LIME) and shapely additive explanation shapely additive values (SHAP). The proposed models are intended to facilitate effective decision-making in clinical research and therapeutic practices.

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