Abstract
Predicting the resistivity of an individual is essential for the optimal and prompt treatment against cardiovascular disease (CVD) in the earlier stage, which recommends the requirement for productive risk evaluation tools. The data-driven-based approach can predict every individual's risk by handling the crucial data patterns. To facilitate clinically applicable CVD prediction resolving the missing data patterns and interpretability issues using machine learning (ML) approaches. Here, a multi-tier model is proposed for mining missing data patterns. Initially, data fusion is adapted to describe the block-wise data patterns. It enables patient data (1) grou**-based feature learning and imputation of missing data and (2) prediction model considering the data availability. The feature selection process uses group characterization to uncover the risk factors. Then, the boosting model is generalized for identifying the patient's sub-group. The experimentation is done on an online available UCI ML dataset to demonstrate the significance of the model compared to various other approaches. The model attains 99% prediction accuracy, which is substantially higher than other approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jensen LJ, Brunak S (2012) Mining electronic health records: Towards better research applications and clinical care. Nat Rev Genet 13(6):395–405
Li Y, Bai C, Reddy CK (2016) A distributed ensemble approach for mining healthcare data under privacy constraints. Inf Sci 330:245–259
Khedr AM, Aghbari ZA, Kamel I (2018) Privacy-preserving decomposable mining association rules on distributed data. Int J Eng Technol 7(3–13):157–162
Khedr, Bhatnagar R (2014) New algorithm for clustering distributed data using K-means. Comput Inf 33:1001–1022
Subhashini, Jeyakumar MK (2017) OF-KNN technique: an approach for chronic kidney disease prediction. Int J Pure Appl Math 116(24):331–348
Nathan, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R (2018) A novel Gini index decision tree data mining method with neural network classifiers for heart disease prediction. Des Autom Embedded Syst 22(3):225
Hsu, Manogaran G, Panchatcharam P, Vivekanandan S (2018) A new approach for prediction of lung carcinoma using backpropagation neural network with decision tree classifiers. In: Proceedings of the IEEE 8th international symposium on cloud and services computing (SC), Nov 2018, pp 111–115
Ramasamy, Nirmala K (2020) Disease prediction in data mining using association rule mining and keyword-based clustering algorithms. Int J Comput Appl 42(1):1–8
Bakar, Kefli Z, Abdullah S, Sahani M (2011) Predictive models for dengue outbreak using multiple rule-based classifiers. In: Proceedings of the international conference on electrical engineering and informatics, 2011, pp 1–6
Hariharan, Umadevi R, Stephen T, Pradeep S (2017) Burden of diabetes and hypertension among people attending health camps in an urban area of Kancheepuram district. Int J Community Med Public Health 5(1):140
Tun, Arunagirinathan G, Munshi SK, Pappachan JM (2017) Diabetes mellitus and stroke: a clinical update. World J Diabetes 8(6):235–248
Ley, Hamdy O, Mohan V, Hu FB (2014) Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet 383(9933):1999–2007
Meng, Huang Y-X, Rao D-P, Zhang Q, Liu Q (2013) Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J Med Sci 29(2):93–99
Bashir, Qamar U, Khan FH (2016) IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed Inform 59:185–200
Nai-Arun, Moungmai R (2015) Comparison of classifiers for the risk of diabetes prediction. Procedia Comput Sci 69:132–142
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kannan, K.S., Lakshmi Bhargav, A., Anil Kumar Reddy, A., Chandu, R.K. (2023). A Constructive Feature Grou** Approach for Analyzing the Feature Dominance to Predict Cardiovascular Disease. In: Kumar, A., Mozar, S., Haase, J. (eds) Advances in Cognitive Science and Communications. ICCCE 2023. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-8086-2_62
Download citation
DOI: https://doi.org/10.1007/978-981-19-8086-2_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8085-5
Online ISBN: 978-981-19-8086-2
eBook Packages: Computer ScienceComputer Science (R0)