Heart Disease Prediction Machine Learning Model by Chhavi VermaHeart Disease Prediction Machine Learning Model by Chhavi Verma

Heart Disease Prediction Machine Learning Model

Chhavi Verma

Data Visualizer

ML Engineer

Data Analyst

Jupyter

Python

Data

Project Overview:

The Disease Prediction project aims to predict the likelihood of heart disease in patients using machine learning models. This project involves developing a web application that allows users to input patient data and receive predictions on heart disease risk.

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

Prediction done by

KNN Classifier

Decision Tree

Random Forest

Dataset:

Source: UCI Heart Disease Dataset on Kaggle

Entries: 303

Features: 14 health indicators

Skills:

Machine Learning: Expertise in model development and evaluation.

Web Development: Creating user-friendly interfaces for model deployment.

Tools:

Python: For data analysis and machine learning using libraries like Pandas, Scikit-Learn, and Seaborn.

Jupyter Notebook: For exploratory data analysis and prototyping models.

Flask/Django: For developing the web application.

HTML/CSS/JavaScript: For front-end development.

AWS/Azure: For cloud deployment.

GitHub: For version control and project sharing.

Exploratory Data Analysis:

Data Exploration: Analyzed the dataset to understand the distribution of features and target classes.

Visualization: Used Seaborn to visualize correlations and distributions.

Feature Selection and Preprocessing:

Feature Engineering: Identified and selected the most relevant features.

Data Processing: Scaled numerical features and converted categorical variables into dummy variables.

Machine Learning Models:

K Neighbors Classifier (K=12): Achieved a mean accuracy of approximately 85%.

Random Forest Classifier: Achieved a mean accuracy of approximately 82%.

Decision Tree Classifier: Achieved a mean accuracy of approximately 73%.

Results:

K Neighbors Classifier: Best performance with mean accuracy ≈ 85%.

Random Forest Classifier: Mean accuracy ≈ 82%.

Decision Tree Classifier: Mean accuracy ≈ 73%.

Next Steps:

Model Optimization: Further fine-tuning and hyperparameter optimization to improve model performance.

Extended Analysis: Exploring additional classifiers and advanced techniques for better accuracy.

Lessons Learned:

Feature Engineering: Crucial for enhancing model performance.

Model Comparison: Importance of evaluating multiple classifiers and tuning their hyperparameters.

Check out the full project on GitHub

#MachineLearning #DataScience #HeartDiseasePrediction #AI #GitHub

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Posted May 29, 2024

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