The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

T R, Mahesh; V, Vinoth Kumar; V, Dhilip Kumar; Geman, Oana; Margala, Martin; Guduri, Manisha

doi:10.1016/j.health.2023.100247

Cited by 20 publications

(2 citation statements)

References 21 publications

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“…In this study, the evaluation used cross fold validation. It was used to assess models or algorithms and divide the data into training data and testing data [28]. This technique was often adopted in this study because it was proven to reduce bias in sampling.…”

Section: Discussionmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

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Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.

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Section: Discussionmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

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show abstract

“…In synthesizing this literature survey, it becomes evident that decision tree modeling offers a robust framework for comprehensively analyzing breast cancer risk factors. However, gaps remain, warranting further exploration into specific subpopulations, temporal dynamics, and the integration of emerging biomarkers [25][26][27][28][29][30]. As we embark on our study, this literature survey provides a foundation for understanding the current state of research, guiding our efforts to contribute novel insights to this dynamic field.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

2023

autml

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Breast cancer remains a significant global health concern, emphasizing the need for comprehensive investigations into its risk factors. This study employs decision tree modeling to analyze the impact of various factors on breast cancer incidence, aiming to contribute valuable insights for prevention and early intervention. Our findings reveal compelling patterns in breast cancer risk factors, shedding light on key variables that significantly influence susceptibility. Through rigorous decision tree modeling, we identify high-risk groups and highlight novel associations that warrant attention. The implications of these findings extend to both clinical practice and public health initiatives, providing a foundation for targeted prevention strategies and personalized healthcare approaches. This study not only enhances our understanding of breast cancer etiology but also underscores the utility of decision tree modeling in unraveling complex relationships within large datasets. As the field of breast cancer research continues to evolve, the insights presented here pave the way for future investigations, emphasizing the importance of tailored risk assessment and intervention strategies.

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Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Zahid,

Tayara,

Chong

2024

Arch Toxicol

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

Cited by 20 publications

References 21 publications

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

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