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https://github.com/teja-1403/heart-disease-analysis-in-python
A comparison of model performance including SVM, Decision Trees, Random Forest and XGBoost on heart.csv using Python.
https://github.com/teja-1403/heart-disease-analysis-in-python
machine-learning python
Last synced: 25 days ago
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A comparison of model performance including SVM, Decision Trees, Random Forest and XGBoost on heart.csv using Python.
- Host: GitHub
- URL: https://github.com/teja-1403/heart-disease-analysis-in-python
- Owner: teja-1403
- Created: 2024-11-30T07:08:11.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-30T07:31:18.000Z (about 1 month ago)
- Last Synced: 2024-11-30T08:23:34.696Z (about 1 month ago)
- Topics: machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 761 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Heart-Disease-Analysis-in-Python
# **Problem:**
In this project, we delve into a dataset encapsulating various health metrics from heart patients, including age, blood pressure, heart rate, and more. Our goal is to develop a predictive model capable of accurately identifying individuals with heart disease. Given the grave implications of missing a positive diagnosis, our primary emphasis is on ensuring that the model identifies all potential patients, making recall for the positive class a crucial metric.
# **Objectives:**
- Explore the Dataset: Uncover patterns, distributions, and relationships within the data.
- Conduct Extensive Exploratory Data Analysis (EDA): Dive deep into bivariate relationships against the target.
- Preprocessing Steps:
- Remove irrelevant features
- Address missing values
- Treat outliers
- Encode categorical variables
- Transform skewed features to achieve normal-like distributions
- Model Building:
- Establish pipelines for models that require scaling
- Implement and tune classification models including SVM, Decision Trees, and Random Forest, XGBoost
- Emphasize achieving high recall for class 1, ensuring comprehensive identification of heart patients
- Evaluate and Compare Model Performance: Utilize precision, recall, and F1-score to gauge models' effectiveness.
# **Dataset:**
The Heart Disease UCI dataset is available on Kaggle, where it is typically used for predictive modeling tasks related to heart disease classification. You can find the heart.csv file that we have used in our project [here](https://drive.google.com/drive/folders/1CR-IgU5OoE-q7kUxyzszdv_tb-9wZega?usp=sharing).
- Dataset Name: Heart Disease Prediction
- Link: [Heart Disease UCI Dataset on Kaggle](https://www.kaggle.com/datasets/arezaei81/heartcsv)
- Description: This dataset is a cleaned version of the original Heart Disease dataset, sourced from the Cleveland Clinic Foundation, and contains various features related to the health of patients, which can be used to predict the presence of heart disease.
This dataset includes the following features:
- age: The age of the patient.
- sex: Gender of the patient (0: female, 1: male).
- cp: Type of chest pain.
- trestbps: Resting blood pressure.
- chol: Serum cholesterol.
- fbs: Fasting blood sugar > 120 mg/dl.
- restecg: Resting electrocardiographic results.
- thalach: Maximum heart rate achieved.
- exang: Exercise induced angina.
- oldpeak: ST depression induced by exercise relative to rest
Numerical: age, trestbps, chol, thalach, oldpeak, etc.
Categorical: sex, cp, fbs, restecg, slope, ca, thal, etc.
Target: target (presence of heart disease: 0 = no, 1 = yes).
# **Result:**
📍 SVM (Support Vector Machine) Model shows the highest recall (0.97) for detecting heart disease, indicating that it performs well in identifying patients with the disease. Despite the high recall, the model maintains a balanced precision, ensuring that false positives are not excessively high.
📍 Random Forest outperforms Decision Tree in terms of overall accuracy and recall, providing more robust predictions. It’s more effective at detecting heart disease with less sensitivity to overfitting, making it a more reliable model for this problem.
📍 XGBoost also performs well, showing a strong recall similar to Random Forest, but its slight improvement in precision and recall makes it a more competitive model for predicting heart disease.
📍 Decision Tree has lower performance compared to the other models, with both accuracy and recall lagging behind Random Forest and SVM. It seems more prone to overfitting, especially when hyperparameters aren't finely tuned.
📍 Box-Cox Transformation significantly improved the distribution of skewed features, such as oldpeak, chol, and trestbps, enhancing the model's ability to detect meaningful patterns in the data.
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