Low-rank and sparse representation based learning for cancer survivability prediction

This project hosts the codes for the paper:

[Low-rank and sparse representation based learning for cancer survivability prediction] Jie Yang, Jun Ma, Khin Than Win, Junbin Gao, Zhenyu Yang Accepted by Information Science

Abstract

Cancer survivability prediction has been of great interest to health professionals and researchers. The task refers to the procedure of estimating the potential survivability according to an individual's medical history. The difficulty is that raw data is usually subject to some noise, such as missing values. To address this issue, we propose a novel low-rank and sparse representation-based learning algorithm, which consists of two main stages of data self expressiveness and classification. Firstly, in the data self expressiveness stage, raw inputs have been decomposed into one dictionary (which is enforced with a low-rank constraint) and one coefficient matrix (which is sparsely coded), respectively. Secondly, this sparse coefficient matrix is paired with sample labels for training during the classification stage. We further integrate these two stages and formulate them into an optimization problem, which is then solved using an iterative computational strategy. Theoretically, we analyze the convergence of the proposed algorithm. The connection and difference between the proposed algorithm and existing approaches are also discussed. The efficiency of the proposed algorithm is experimentally verified using several benchmarking classification problems and a public longitudinal dataset. Experimental results demonstrate that the proposed algorithm achieves superior performance in terms of affordable computational complexity and high prediction accuracy, compared to state-of-the-art approaches.

Algorithm

Dataset

• This project is mainly based on the open-source SEER data (https://seer.cancer.gov/).

• Please refer to the FileDescription.pdf for more descriptoin about the installation and dataset preparation.

• We also employ a few benchmarking problems from the UCI repository, including Hepatitis, Liver, Blood, Mammographic, Loan, Tae, Cleveland, CMC, Wine, and Abalone.

Result

List of selected variables/features from SEER for prediction modelin

Feature	Description	Unique Values
REG	Registry ID	8
MAR_STAT	Marital status at diagnosis	6
RACE	Ethnicity	29
ORIGIN	Spanish/Hispanic Origin	10
SEX	Gender	2
YR_BRTH	Year of birth	111
DATE_YR	Year of diagnosis	40
SEQ_NUM	Sequence of all reportable malignant	2
SITEO2V	Primary site	9
LATERAL	Laterality	5
SURGPRIM	Surgery of primary site	7
NO_SURG	Reason no cancer-directed surgery	8
RADIATN	Method of radiation therapy	10
RAD_SURG	Radiation sequence with surgery	7
NUMPRIMS	Number of primaries	6
HISTREC	Histology	7
ERSTATUS	Tumor marker 1	5
PRSTATUS	Tumor marker 2	5
ADJTM_6	Breast Adjusted AJCC 6th T	16
ADJNM_6	Breast Adjusted AJCC 6th N	7
ADJM_6	Breast Adjusted AJCC 6th M	5
ADJAJCCSTG	Breast Adjusted AJCC 6th Stage	12
SRV_TIME_MON	Survival Months (dependent variable)	447

Comparison with Existing Methods

Algorithm	Train	Testing
SRC	-	69.60%
LC-KSVD	83.77%	70.75%
ISSRC	-	68.06%
IRPCA	81.82%	61.57%
ROSL	82.47%	78.99%
LRVD-IPGSR	-	63.45%
Proposed	84.20%	81.41%