ELM and LIGHTGBM: A Hybrid Machine Learning Technique with Intelligent IOT to Predict the cardiovascular disease

Authors

Gorapalli Srinivasa Rao, Research Scholar, School of Computer Science and Engineering, VIT-AP University, Amaravathi, IndiaFollow
G Muneeswari, Professor, School of Computer Science and Engineering, VIT-AP University, Amaravathi, IndiaFollow

Abstract

Cardiologists can more accurately classify a patient's condition by performing an accurate diagnostic and prognosis of cardiovascular disease (CVD). The clinical diagnosis, and therapies processes within the medical field have been substantially accelerated by ML-based approaches enabled by IoT-based systems. This structure is based on IoT-based system with enabled ML approach. This study investigates an approach known as ensemble categorization, which enhances the precision of weak algorithms by integrating multiple classifiers. For effective CVD classification, we utilized Ensemble learning machine (ELM) and Light GBM. The appropriate traits are chosen to speed up the categorization process using the Gorilla Troops Optimizer technique. The investigation findings demonstrate that ensemble techniques are beneficial in improving the predictive ability of weak categorizers.

Recommended Citation

Rao, Gorapalli Srinivasa and Muneeswari, G (2024) "ELM and LIGHTGBM: A Hybrid Machine Learning Technique with Intelligent IOT to Predict the cardiovascular disease," Karbala International Journal of Modern Science: Vol. 10 : Iss. 2 , Article 9.
Available at: https://doi.org/10.33640/2405-609X.3354

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