Empowering Heart Health: A Machine Learning Solution with Telemedicine Integration for Early Detection and Treatment of Cardiovascular Conditions

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Varad Nikam; Shivangi Pandey; Vipul Ojha; Ayush verma; Shruti Narad; Shanu Khare

Empowering Heart Health: A Machine Learning Solution with Telemedicine Integration for Early Detection and Treatment of Cardiovascular Conditions

Authors: Varad Nikam, Shivangi Pandey, Vipul Ojha, Ayush verma, Shruti Narad, Shanu Khare

Unique Paper ID: 169408
Volume: 11
Issue: 6
PageNo: 984-988

Keywords: SVM KNN EDA Random Forest machine learning and dataset.

Abstract:
In this paper we have discussed over the development of a comprehensive machine learning-based prediction model for the diagnosis and management of cardiac disease. We use fifteen factors, such as clinical signs, medical history, and demographics. Following feature engineering and data pretreatment, we examine many strategies to determine which works best. Accuracy is further improved via ensemble approaches. With Apollo 24*7 telemedicine incorporated into our paradigm, real-time consultation is possible. Compliance is ensured with a focus on security and privacy. Our platform seeks to enhance the results and accessibility of healthcare for people who are at risk of heart disease.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 984-988

Empowering Heart Health: A Machine Learning Solution with Telemedicine Integration for Early Detection and Treatment of Cardiovascular Conditions

Available:https://ijirt.org/article?manuscript=169408

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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