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Illustrating a Scalable Architecture-Powered Disease Prediction Using Machine Learning Techniques


Affiliations
1 Centre for Distance and Online Education, Bharathidasan University, India., India
2 Department of Computer Science, Government Arts and Science College, Srirangam, Tiruchirappalli, India., India
3 Department of Computer Applications, Holy Cross College, India., India
4 Edge AI Division, Reliance Jio Platforms Ltd., Bangalore, India., India
     

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Healthcare information systems typically collect, store and manage various kinds of data such as illness details, clinical history, essential body parameters, health insurance plans, and other related data towards enabling data processing and analytics to arrive at better decision making with all the clarity and alacrity. To reduce the mortality rate due to heart diseases, it is essential to predict the presence of disease in its budding stage itself. Manual extraction of the useful knowledge from historical data is practically tedious and timeconsuming. Machine learning (ML) algorithms are being used to detect and predict something useful out of both historical and current data. Despite the applicability of machine learning algorithms for prediction, the accuracy of prediction is significantly influenced by features used for prediction. Moreover, to meet the needs of evolving data sizes, suitable technologies for data storage also become essential. Based on these two aspects, a comparative analysis has been performed for feature selection using four filter methods, namely, correlation measure, information gain, gain ratio and relief. Further, a scalable architecture using Hadoop framework has been proposed to enable the machine learning algorithms to handle larger datasets while performing prediction task. The impact of the proposed architecture on the performance of machine learning algorithm has been evaluated with benchmark dataset and found to have improved scalability and accuracy.

Keywords

Disease Prediction, Hadoop Distributed File System, Machine Learning, Random Forest, Support Vector Machine, Scalable Architecture.
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  • Illustrating a Scalable Architecture-Powered Disease Prediction Using Machine Learning Techniques

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Authors

Chellammal Surianarayanan
Centre for Distance and Online Education, Bharathidasan University, India., India
Sharmila Rengasamy
Department of Computer Science, Government Arts and Science College, Srirangam, Tiruchirappalli, India., India
M. Baby Nirmala
Department of Computer Applications, Holy Cross College, India., India
Pethuru Raj Chelliah
Edge AI Division, Reliance Jio Platforms Ltd., Bangalore, India., India

Abstract


Healthcare information systems typically collect, store and manage various kinds of data such as illness details, clinical history, essential body parameters, health insurance plans, and other related data towards enabling data processing and analytics to arrive at better decision making with all the clarity and alacrity. To reduce the mortality rate due to heart diseases, it is essential to predict the presence of disease in its budding stage itself. Manual extraction of the useful knowledge from historical data is practically tedious and timeconsuming. Machine learning (ML) algorithms are being used to detect and predict something useful out of both historical and current data. Despite the applicability of machine learning algorithms for prediction, the accuracy of prediction is significantly influenced by features used for prediction. Moreover, to meet the needs of evolving data sizes, suitable technologies for data storage also become essential. Based on these two aspects, a comparative analysis has been performed for feature selection using four filter methods, namely, correlation measure, information gain, gain ratio and relief. Further, a scalable architecture using Hadoop framework has been proposed to enable the machine learning algorithms to handle larger datasets while performing prediction task. The impact of the proposed architecture on the performance of machine learning algorithm has been evaluated with benchmark dataset and found to have improved scalability and accuracy.

Keywords


Disease Prediction, Hadoop Distributed File System, Machine Learning, Random Forest, Support Vector Machine, Scalable Architecture.

References