Predict the Performance Analysis of Supervised Learning Techniques Using Heart Disease Database

Rao, Srikantha; Reddy, Pratap; Srinivas, V. Sesha; Kumar, B. Senthil

doi:10.1109/incet51464.2021.9456206

Cited by 2 publications

(1 citation statement)

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“…The application of supervised learning techniques [8], [9] to heart disease databases enables accurate heart disease prediction, with performance analysis providing insights into the effectiveness of different algorithms. An evaluation metric's capacity to correctly categories patients is measured by things like accuracy, precision, recall, and F1-score.…”

Section: Related Workmentioning

confidence: 99%

Smart heart disease prediction and amalgamation tracking system

Shanmugaraj,

Nagaradjane,

Candassamy

et al. 2024

Eng. rev. (Online)

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In this modern world, cardiovascular disease stands as the leading cause of global mortality. To combat this alarming trend and prevent the devastating loss of lives, an innovative solution that focus on reliability, accuracy, scalability, and cost-effectiveness. This work proposes a system that utilizes an artificial intelligence processor (LSAI48266X) and an IoT device to transfer data from sensors such as the Mach30100 and DS18B20. The system aims to track, visualize, and forecast heart disease. Random Forest is a machine learning algorithm that predicts cardiac illness based on numerous parameters such as SpO2, heartbeat, temperature, and blood pressure. Web application is developed using PHP that displays hospital details and integrated with a Telegram chatbot for communication during emergency conditions. Compared to earlier methods, our proposed system distinguishes itself with an impressive accuracy rate of 95.6% and automation system to prevent loss of human life by incorporating the Random Forest Algorithm and tracking system.

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Section: Related Workmentioning

confidence: 99%