Predictive Model for the Monitoring and Detection of Heart Disease using Wavelet Based Machine Learning Technique

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Predictive Model for the Monitoring and Detection of Heart Disease using Wavelet Based Machine Learning Technique

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  • October 7, 2022
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ABSTRACT

This research paper on Predictive Model for the monitoring and detection of heart disease using wavelet-based machine learning technique is aimed at enhancing and easing the process of detecting heart diseases efficiently and in an automated manner. This paper adopts the Dynamic Systems Development Model (DSDM) methodology which was originally based on the Rapid Application Development Methodology. This methodology is applied for a fast delivery of the new system within a specified work plan, budget and time. The methodology also features iterative phases such as feasibility and business study, functional and mathematical modelling, implementation and simulations. Artificial Neural Network technique was also integrated with wavelet technique in this study for a clearer productivity and noise reduction during data processing. Results from the simulation of this work were validated using 10-fold validation technique and the Mean Squared Error of 0.0005423 on the average and a regression of 0.9978 on the average performance were achieved.

Keywords: Heart Disease; Artificial Neural Network; Wavelet Based Machine Learning Technique; Predictive Model

Authorship: Nnenna, H. N., Odo, H., Ozoemena, P., & Uzoka, E. C. | FULL PDF

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