Implementation of Deep Learning Method for Prediction of Cardiovascular Disease Data Science FYP/ Thesis Idea

Project Domain / Category

Artificial Intelligence: Deep learning

Abstract/Introduction

Cardiovascular disease (CVD) is a general term for conditions affecting the heart or blood vessels, world Health Organization (WHO

https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

The program will be implemented to predict the Cardiovascular Disease by using deep learning methods. In this system, it will be considered requirements that utilize UCI machine learning repository dataset for experimentation ( particular Cardiovascular Disease dataset ) as given below links provided.

Functional Requirements:

  1. There are seven major tasks you will typically perform when developing a system. Tasks (2-7) should be implemented internally while developing the
  • Task 1: Define the problem and select the dataset as any type of Cardiovascular Disease
  • Task 2: Data Analysis and Preprocessing
  • Task 3: Feature Extraction
  • Task 4: Prediction
  • Task 5: Build system
  • Task 6: Test System
  • Task 7: Tune System
  1. The program should have a knowledge-based system according to select data (Structured or Unstructured data).
  2. The program should have the deep learning algorithm to execute model
  3. The program should evaluate the performance and update knowledge based on the

Tools/language:

       Python language

DataSet: https://archive.ics.uci.edu/ml/datasets/heart+disease

https://www.kaggle.com/ronitf/heart-disease-uci

https://ieee-dataport.org/documents/cardiovascular-disease-dataset

 Prerequisite:

Deep Learning Concepts, students will cover a short course relevant to the mentioned concepts besides SRS and design initial documentation.

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