TY - JOUR
T1 - Predicting overall survivability in comorbidity of cancers
T2 - A data mining approach
AU - Zolbanin, Hamed Majidi
AU - Delen, Dursun
AU - Hassan Zadeh, Amir
N1 - Funding Information:
Cancer and other chronic diseases are increasingly absorbing healthcare expenditures all over the world. Yet even worse, comorbidity of such diseases thwarts the efforts made for their diagnosis and treatment, spawning more economic losses. One way to alleviate this complication is to shift from a reductionist approach in studying the diseases in isolation to the consideration of their interactions. By combining records of patients suffering simultaneously from two cancers, we created a comorbid set of cancer patients. We analyzed the resultant data regardless of the patients' final outcomes, i.e., whether they survived, died as a result of the cancer(s) they suffered from, or died due to other diseases or reasons. As opposed to some studies which build prediction models on disease-specific outcomes (i.e., only those patients who die from the adverse effects of the disease under study), we believe our treatment of the data sets is more realistic, as a true prediction model should not and could not use any variables that somehow relate to the desired target. Consequently, while our models might have lower accuracy rates compared to some existing models, it is of greater predictive value. Our models cannot only be used to predict the patients' outcomes with fairly acceptable accuracy rates, they can also help practitioners make better decisions on the course of treatment. More importantly, our results show that to obtain even better accuracy rates, disease registries should record patients' concomitant diseases so that prospective analyses to build better models and find insightful patterns become possible or at least, less costly. This study shows the importance of comorbidity related data in analysis and treatment of chronic diseases. In addition to its theoretical contribution to the extant body of knowledge, the findings of this research effort can also be extended to potential managerial and practical implications. Evidence-based (data-based) medical decision making is among the most important tools we currently have to improve the wellbeing of people while controlling healthcare costs. The use of decision support systems that have a holistic view (by considering multiple conditions or diseases and their interactions) towards diagnostic and treatments would pave the road towards more effective and efficient healthcare systems. Our results, once again, underscore the importance of comorbidities in studies of cancers and other chronic diseases. Researchers working in these fields can benefit from our conclusions in gaining a better understanding of the impact and role of concurrent complications. Specifically, our results show that identifying significant variables in each cancer and building clinical decision support systems should not be conducted without paying a closer attention to comorbid conditions. Hamed Majidi Zolbanin is a PhD student in Management Science and Information Systems at Oklahoma State University. He holds degrees in Computer Engineering and in Management. His research interests include healthcare, machine learning, data and text mining, and behavioral studies. Dr. Dursun Delen is the holder of William S. Spears and Neal Patterson Endowed Chairs in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). He received his PhD in Industrial Engineering and Management from OSU in 1997. Prior to his appointment as an Assistant Professor at OSU in 2001, he worked for a privately-owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for 5 years, during which he led a number of decision support, information systems and advanced analytics related research projects funded by federal agencies, including DoD, NASA, NIST and DOE. His research has appeared in major journals including Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, Expert Systems with Applications, among others. He recently published seven books in the broader are of Business Analytics. He is often invited to national and international conferences for keynote addresses on topics related to Data/Text Mining, Business Intelligence, Decision Support Systems, Business Analytics and Knowledge Management. He regularly serves and chairs tracks and mini-tracks at various information systems conferences, and serves on several academic journals as senior editor, associate editor and editorial board member. His research and teaching interests are in data and text mining, decision support systems, knowledge management, business intelligence and enterprise modeling. Amir Hassan Zadeh is an assistant professor of Information Systems and Supply Chain Management at Wright State University. He has previously published papers in Decision Support Systems, Production Planning & Control, Annals of Information Systems. He has also presented several papers at national and international meetings. His research interests include data mining, business analytics, decision support systems, and operations management.
Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.
PY - 2015/4/19
Y1 - 2015/4/19
N2 - Cancer and other chronic diseases have constituted (and will do so at an increasing pace) a significant portion of healthcare costs in the United States in recent years. Although prior research has shown that diagnostic and treatment recommendations might be altered based on the severity of comorbidities, chronic diseases are still being investigated in isolation from one another in most cases. To illustrate the significance of concurrent chronic diseases in the course of treatment, this study uses SEER's cancer data to create two comorbid data sets: one for breast and female genital cancers and another for prostate and urinal cancers. Several popular machine learning techniques are then applied to the resultant data sets to build predictive models. Comparison of the results shows that having more information about comorbid conditions of patients can improve models' predictive power, which in turn, can help practitioners make better diagnostic and treatment decisions. Therefore, proper identification, recording, and use of patients' comorbidity status can potentially lower treatment costs and ease the healthcare related economic challenges.
AB - Cancer and other chronic diseases have constituted (and will do so at an increasing pace) a significant portion of healthcare costs in the United States in recent years. Although prior research has shown that diagnostic and treatment recommendations might be altered based on the severity of comorbidities, chronic diseases are still being investigated in isolation from one another in most cases. To illustrate the significance of concurrent chronic diseases in the course of treatment, this study uses SEER's cancer data to create two comorbid data sets: one for breast and female genital cancers and another for prostate and urinal cancers. Several popular machine learning techniques are then applied to the resultant data sets to build predictive models. Comparison of the results shows that having more information about comorbid conditions of patients can improve models' predictive power, which in turn, can help practitioners make better diagnostic and treatment decisions. Therefore, proper identification, recording, and use of patients' comorbidity status can potentially lower treatment costs and ease the healthcare related economic challenges.
KW - Comorbidity
KW - Concomitant diseases
KW - Concurrent diseases
KW - Medical decision making
KW - Predictive modeling
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=84928949248&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2015.04.003
DO - 10.1016/j.dss.2015.04.003
M3 - Article
AN - SCOPUS:84928949248
SN - 0167-9236
VL - 74
SP - 150
EP - 161
JO - Decision Support Systems
JF - Decision Support Systems
ER -