Prediction of Heart Disease and Diabetes (HDD) using Self-Adaptive Particle Swarm Optimization- Based Random Forest Algorithm(SAPSORF)
Prediction of Heart Disease and Diabetes (HDD) using Self-Adaptive Particle Swarm Optimization- Based Random Forest Algorithm(SAPSORF) |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-6 |
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| Year of Publication : 2023 | ||
| Author : S. Usha, S. Kanchana |
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| DOI : 10.14445/22315381/IJETT-V71I6P240 | ||
How to Cite?
S. Usha, S. Kanchana, "Prediction of Heart Disease and Diabetes (HDD) using Self-Adaptive Particle Swarm Optimization- Based Random Forest Algorithm(SAPSORF)," International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 406-420, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I6P240
Abstract
Heart Disease and Diabetes (HDD) is widely recognized as the most lethal conditions afflicting humans. Preventing and treating HDDs requires accurate risk assessment at an early stage. Experts have created several machines learning-based intelligent systems to diagnose HDD automatically to address this problem. However, their classification accuracy is still below par. Furthermore, most existing machine learning models are tailored toward predicting certain diseases, such as cardiovascular disease, diabetes, lung illness, etc. For this reason, a classifier that can reliably predict the occurrence of several diseases is desirable. This paper proposes the Self-Adaptive Particle Swarm Optimization-based Random Forest Algorithm (SAPSORF) to predict cardiovascular and diabetes disease. The performance of the modified Random Forest Algorithm is enhanced via a bio-inspired algorithm, namely Self-Adaptive Particle Swarm Optimization. SAPSORF enriches sampling and dimensionality reduction phases of modified random forest. This study assesses the effectiveness of the proposed classifier on two distinct datasets: the Cardiovascular Disease Dataset and the PIMA Indian Diabetes Dataset. The evaluation results indicate that the proposed classifier surpasses existing classifiers in terms of accuracy when it comes to classification tasks.
Keywords
Diabetes, Heart Disease, Optimization, Particle Swarm, Random Forest.
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