Cardiovascular Disease Prediction using machine learning

Alapati, Naresh; Prasad, B.V.V.S.; Sharma, Aditi; Kumari, G.R.P.; Bhargavi, Perumalla Jaya; Alekhya, Arikatla; Pravallika, Botla; Nandini, Kilari

doi:10.1109/icfirtp56122.2022.10059422

Cited by 1 publication

(1 citation statement)

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“…Consequently, they present the potential for enhanced prediction models [27]. Research investigations that have employed machine learning techniques to predict the risk of CVD have exhibited encouraging outcomes, showcasing the potential to improve the accuracy of risk assessment [28].…”

Section: The Rise Of Machine Learning In Healthcarementioning

confidence: 99%

A machine learning approach to cardiovascular disease prediction with advanced feature selection

Abdullahi,

Ali Barre,

Hussein Elmi

2024

IJEECS

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<div align="center">Cardiovascular diseases (CVDs) pose a significant global public health challenge, necessitating precise risk assessment for proactive treatment and optimal utilization of healthcare resources. This study employs machine learning algorithms and sophisticated feature selection techniques to enhance the accuracy and comprehensibility of CVD prediction models. While traditional risk assessment tools are valuable, they frequently fail to consider the myriad intricate factors that contribute to the heightened risk of CVD. Our methodology employs machine learning algorithms to analyze diverse healthcare data sources and produce advanced predictive models. The salient feature of this research lies in the meticulous application of advanced feature selection techniques, enabling the identification of pivotal factors within heterogeneous datasets. Optimizing feature selection enhances the interpretability of the model, reduces dimensionality, and improves predictive accuracy. The area under the ROC curve (AUC-ROC) score of the wrapper method model significantly decreased from 95.1% to 75.1% after tuning, based on empirical tests that supported the suggested method. This showcases its capacity as a tool for assessing premature CVD susceptibility and developing tailored healthcare strategies. The study highlights the significance of integrating machine learning with feature selection due to the widespread influence of cardiovascular diseases. Integrating this system has the potential to enhance patient care and optimize the utilization of healthcare resources.</div>

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Section: The Rise Of Machine Learning In Healthcarementioning

confidence: 99%