Description of MQ-6 Combustible, Propane, Butane, LPG Gas Sensor Module

Objective: The primary objective of this research is to develop a sophisticated deep learning model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) to enhance the accuracy and efficiency of cardiovascular disease (CVD) detection. The goal is to provide a non-invasive, cost-effective, and reliable diagnostic tool that leverages advanced AI capabilities to predict and diagnose CVDs at early stages, thereby facilitating timely intervention and improving patient outcomes.

Proposed Work: The proposed work introduces an innovative approach using a Bi-LSTM model, which is adept at handling sequential and time-series data, crucial for CVD detection. By employing an Infinite Feature Selection (IFS) technique, the model efficiently processes data from the UCI ML repository, emphasizing the most significant features for CVD prediction. This methodology aims to overcome the limitations of existing models by reducing complexity and enhancing predictive accuracy.

Problem Definition: The research addresses the critical challenge of early and accurate detection of cardiovascular diseases, which are the leading cause of death worldwide. Current detection methods are either invasive, costly, or lack precision. There is a pressing need for an advanced, non-invasive diagnostic tool that can process vast datasets and accurately predict CVDs, thus bridging the gap in the healthcare domain.

Application Area: The application of this research spans across healthcare, particularly in cardiology, where there is a significant demand for advanced diagnostic tools. It can be integrated into healthcare systems for routine screenings or monitoring, aiding physicians in making informed decisions and offering timely treatment to patients with or at risk of CVDs.

Keywords: Cardiovascular Diseases, Deep Learning, Bi-LSTM, Feature Selection, Non-Invasive Diagnosis, Healthcare, Artificial Intelligence.

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