Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Sheau-Ling Hsieh; Sung-Huai Hsieh; Po-Hsun Cheng; Chi-Huang Chen; Kai-Ping Hsu; I-Shun Lee; Zhenyu Wang; Fei-Pei Lai

doi:10.1007/s10916-011-9762-6

Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Sheau-Ling Hsieh, Sung-Huai Hsieh, Po-Hsun Cheng, Chi-Huang Chen, Kai-Ping Hsu, I-Shun Lee, Zhenyu Wang, Fei-Pei Lai

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Abstract

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

Original language	English
Pages (from-to)	2841-2847
Number of pages	7
Journal	Journal of Medical Systems
Volume	36
Issue number	5
DOIs	https://doi.org/10.1007/s10916-011-9762-6
State	Published - Oct 2012

Keywords

Ensemble learning; Neural fuzzy; KNN; Quadratic classifier; Information gain

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title = "Design Ensemble Machine Learning Model for Breast Cancer Diagnosis",

abstract = "In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.",

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AU - Hsieh, Sheau-Ling

AU - Hsieh, Sung-Huai

AU - Cheng, Po-Hsun

AU - Chen, Chi-Huang

AU - Hsu, Kai-Ping

AU - Lee, I-Shun

AU - Wang, Zhenyu

AU - Lai, Fei-Pei

PY - 2012/10

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N2 - In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

AB - In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

KW - Ensemble learning; Neural fuzzy; KNN; Quadratic classifier; Information gain

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DO - 10.1007/s10916-011-9762-6

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