❤️ Heart Disease Prediction and Identification with Deep Learning and Neural Networks

This repository contains the implementation of heart disease detection using Deep Learning and Neural Networks. The work focuses on analyzing and predicting heart conditions using a medical dataset and Python-based tools.

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📘 Proposal

Heart-related diseases are a leading cause of death worldwide. This project leverages deep learning algorithms to identify abnormalities from ECG data and predict heart diseases, assisting healthcare professionals in early diagnosis.

Master cardiologists can classify signals into:

• Typical
• AF
• Tachycardia
• Bradycardia
• Arrhythmia
• Other
• Boisterous

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🎯 Objective

To detect heart disease at an early stage using available attributes from a heart disease dataset.

• Apply preprocessing to remove null/noisy data.
• Visualize and analyze clean data.
• Use Deep Learning algorithms for training and testing (70% train, 30% test).

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💡 Motivation

Develop a Python-based deep learning application that helps detect heart disease in the early stages for timely treatment.

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📈 Significance

• Deep learning improves prediction accuracy.
• Useful in real-world clinical environments.
• Can be executed via Google Colab without local installation.

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🧱 System Architecture

graph TD
    A[Heart Disease Dataset] --> B[Data Preprocessing]
    B --> C[Data Cleaning]
    C --> D[Train-Test Split]
    D --> E[Deep Learning Algorithm]
    E --> F[Heart Disease Prediction]

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