Data-driven versus a domain-led approach to k-means clustering on an open heart failure dataset

Abstract

Domain-driven data mining of health care data poses unique challenges. The aim of this paper is to explore the advantages and the challenges of a ‘domain-led approach’ versus a data-driven approach to a k-means clustering experiment. For the purpose of this experiment, clinical experts in heart failure selected variables to be used during the k-means clustering, whilst during the ‘data-driven approach’ feature selection was performed by applying principal component analysis to the multidimensional dataset. Six out of seven features selected by physicians were amongst 26 features that contributed most to the significant principal components within the k-means algorithm. The data-driven approach showed advantage over the domain-led approach for feature selection by removing the risk of bias that can be introduced by domain experts. Whilst the ‘domain-led approach’ may potentially prohibit knowledge discovery that can be hidden behind variables not routinely taken into consideration as clinically important features, the domain knowledge played an important role at the interpretation stage of the clustering experiment providing insight into the context and preventing far fetched conclusions. The “data-driven approach” was accurate in identifying clusters with distinct features at the physiological level. To promote the domain-led data mining approach, as a result of this experiment we developed a practical checklist guiding how to enable the integration of the domain knowledge into the data mining project.

Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

“…Beyond model-driven research thus suggests new perspectives. Examples include domain-driven [31] and model-free research, and semi-model-based research which partially involves the model and then further explores the genuine models fitting the problem well. Modeling problem characteristics and complexities is another example.…”

Recent advances in domain-driven data mining

“…Incorporating domain knowledge for unsupervised learning is particularly challenging due to the lack of clearly defined learning target. In the domain of healthcare, Jasinska-Piadlo et al [22] explored the advantages and the challenges of a 'domain-led' approach versus a data-driven approach to K-means clustering analysis. The authors compared expert opinions and principal component analysis for selecting the most useful variables to be used for the K-means clustering.…”

“…Another contributor to this proliferation is the increase in the quantity of data collected, stored, and appropriately documented for mining since the benefits of leveraging this data has become more apparent. Some of the works in this special issue demonstrated how data mining techniques can be applied in agriculture [2], healthcare and medicine [22,52], and city planning [19].…”

Recent advances in domain-driven data mining

“…Incorporating domain knowledge for unsupervised learning is particularly challenging due to the lack of clearly defined learning target. In the domain of health care, Jasinska-Piadlo et al [22] explored the advantages and the challenges of a "domain-led" approach versus a data-driven approach to K -means clustering analysis. The authors compared expert opinions and principal component analysis for selecting the most useful variables to be used for the K -means clustering.…”

“…Another contributor to this proliferation is the increase in the quantity of data collected, stored, and appropriately documented for mining since the benefits of leveraging this data has become more apparent. Some of the works in this special issue demonstrated how data mining techniques can be applied in agriculture [2], health care and medicine [22,48], and city planning [19].…”