Heart Disease Prediction

Heart Disease Prediction System built with SKLearn using Deep Learning Neural Networks by applying multiple Deep Learning Algorithms to give the best results. It can predict if the person is suffering from heart disease or not by taking some input data values.The main motivation of doing this research is to present a heart disease prediction model for the prediction of occurrence of heart disease. Further, this research work is aimed towards identifying the best classification algorithm for identifying the possibility of heart disease in a patient. This work is justified by performing a comparative study and analysis using 5 Classification algorithms namely SVC (Support Vector Classifier), KNeighbors, Decision Tree, Gradient Boosting and Random Forest are used at different levels of evaluations. Although these are commonly used machine learning algorithms, the heart disease prediction is a vital task involving highest possible accuracy. Hence, the five algorithms are evaluated at numerous levels and types of evaluation strategies. This will provide researchers and medical practitioners to establish a better.

Features

GUI Support: GUI support is adding to take input using Tkinter.
Support for detection of Heart Disease: our model is able to detect Whether a person is suffering from heart disease or not.

Frameworks and Libraries

SKLearn: Simple and efficient tools for predictive data analysis
Joblibs: Joblib is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays.
Matplotlib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Numpy: Caffe-based Single Shot-Multibox Detector (SSD) model used to detect faces
Pandas: pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Data Preprocessing

Data pre-processing is an important step for the creation of a machine learning model. Initially, data may not be clean or in the required format for the model which can cause misleading outcomes. In pre-processing of data, we transform data into our required format. It is used to deal with noises, duplicates, and missing values of the dataset. Data pre-processing has the activities like importing datasets, splitting datasets, attribute scaling, etc. Preprocessing of data is required for improving the accuracy of the model.

Prerequisites

All the dependencies and required libraries are included in the file requirements.txt See here

Installation

Clone the repo

$ git clone https://github.com/Chaganti-Reddy/Heart_Disease_Prediction.git

Change your directory to the cloned repo

$ cd Heart_Disease_Prediction

Before running the command copy the downloaded dataset folder to face-mask-detector folder…

Now, run the following command in your Terminal/Command Prompt to install the libraries required

$ pip3 install -r requirements.txt

How to Run

Open terminal. Go into the cloned project directory and type the following command:

$ python3 heart_disease_prediction_using_machine_learning.py