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An alternating least square based algorithm for predicting patient survivability

Journal Article


Abstract


  • Breast cancer is the most common cancer to females worldwide. Using machine learning technology to predict breast-cancer patients’ survivability has drawn a lot of research interest. However, it still faces many issues, such as missing-value imputation. As such, the main objective of this paper is to develop a novel imputation algorithm, inspired by the recommendation system. More precisely, features with missing values are regarded as items to be evaluated for recommendation.

    Consequently, a matrix factorisation algorithm (Alternating Least Square, ALS) is employed to replace missing values; accordingly, four different prediction strategies based on the ALS result are further discussed. The proposed ALS-based imputation algorithm is evaluated by using a large patient dataset from the Surveillance, Epidemiology, and End Results (SEER) program. Experimental results demonstrates a significant improvement on the survivability prediction, compared to existing methods.

Publication Date


  • 2019

Citation


  • Hu, Q., Yang, J., Win, K. Than. & Huang, X. (2019). An alternating least square based algorithm for predicting patient survivability. Communications in Computer and Information Science, 996 305-317. Bathurst, Australia Data Mining: 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers

Scopus Eid


  • 2-s2.0-85063459270

Number Of Pages


  • 12

Start Page


  • 305

End Page


  • 317

Volume


  • 996

Place Of Publication


  • Germany

Abstract


  • Breast cancer is the most common cancer to females worldwide. Using machine learning technology to predict breast-cancer patients’ survivability has drawn a lot of research interest. However, it still faces many issues, such as missing-value imputation. As such, the main objective of this paper is to develop a novel imputation algorithm, inspired by the recommendation system. More precisely, features with missing values are regarded as items to be evaluated for recommendation.

    Consequently, a matrix factorisation algorithm (Alternating Least Square, ALS) is employed to replace missing values; accordingly, four different prediction strategies based on the ALS result are further discussed. The proposed ALS-based imputation algorithm is evaluated by using a large patient dataset from the Surveillance, Epidemiology, and End Results (SEER) program. Experimental results demonstrates a significant improvement on the survivability prediction, compared to existing methods.

Publication Date


  • 2019

Citation


  • Hu, Q., Yang, J., Win, K. Than. & Huang, X. (2019). An alternating least square based algorithm for predicting patient survivability. Communications in Computer and Information Science, 996 305-317. Bathurst, Australia Data Mining: 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers

Scopus Eid


  • 2-s2.0-85063459270

Number Of Pages


  • 12

Start Page


  • 305

End Page


  • 317

Volume


  • 996

Place Of Publication


  • Germany