muntahashams / heart-disease-prediction-using-neural-networks

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease

Heart-Disease-Prediction-using-Neural-Networks

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease. This project will utilize a dataset of 303 patients and distributed by the UCI Machine Learning Repository.

Machine learning and artificial intelligence is going to have a dramatic impact on the health field; as a result, familiarizing yourself with the data processing techniques appropriate for numerical health data and the most widely used algorithms for classification tasks is an incredibly valuable use of your time! In this tutorial, we will do exactly that.

We will be using some common Python libraries, such as pandas, numpy, and matplotlib. Furthermore, for the machine learning side of this project, we will be using sklearn and keras. Import these libraries using the cell below to ensure you have them correctly installed.

The dataset is available through the University of California, Irvine Machine learning repository.

Here is the URL:

http:////archive.ics.uci.edu/ml/datasets/Heart+Disease

This dataset contains patient data concerning heart disease diagnosis that was collected at several locations around the world. There are 76 attributes, including age, sex, resting blood pressure, cholestoral levels, echocardiogram data, exercise habits, and many others. To data, all published studies using this data focus on a subset of 14 attributes - so we will do the same. More specifically, we will use the data collected at the Cleveland Clinic Foundation.

To import the necessary data, we will use pandas' built in read_csv() function.