https://github.com/mariiasam/stroke-prediction
A model for predicting the risk of stroke in a patient
https://github.com/mariiasam/stroke-prediction
balanced-random-forest-classifier decission-tree-classifier gradient-boosting imbalanced-learning joblib logistic-regression matplotlib numpy random-forest-classifier scikit-learn seaborn streamlit
Last synced: 6 months ago
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A model for predicting the risk of stroke in a patient
- Host: GitHub
- URL: https://github.com/mariiasam/stroke-prediction
- Owner: MariiaSam
- Created: 2025-01-30T15:48:42.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-16T20:29:27.000Z (7 months ago)
- Last Synced: 2025-03-16T20:36:41.546Z (7 months ago)
- Topics: balanced-random-forest-classifier, decission-tree-classifier, gradient-boosting, imbalanced-learning, joblib, logistic-regression, matplotlib, numpy, random-forest-classifier, scikit-learn, seaborn, streamlit
- Language: Jupyter Notebook
- Homepage: https://stroke-prediction-ms.streamlit.app/
- Size: 17.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Classification model: Stroke Prediction
# A model for predicting the risk of stroke in a patient
This project was developed to determine the likelihood of a patient having a stroke. The interactive interface is based on [**Streamlit**](https://stroke-prediction-ms.streamlit.app/), which allows you to easily interact with the model and analyze the results.
## Description
**1. Project objective:** To create an analytical tool to study the risk of stroke in a patient, taking into account various factors
**2. Project objectives:**
**Data analysis**: to identify the factors that influence to the risk of stroke in a patient.
- data analysis: to identify key factors that influence the risk of stroke;
- model Building: use machine learning and statistical analysis to create a model that can predict stroke risk;
- user Interface: develop an interactive interface that allows users to enter new data, analyze the results, and make predictions based on the model.
## Technologies
The project was implemented using the following technologies:
- **Python**: the main programming language
## Libraries
- **Pandas**: for data processing;
- **Numpy**: for numerical calculations;
- **Scikit-learn**: for building and evaluating machine learning models;
- **Imbalanced-learn**: provides tools when dealing with classification with imbalanced classes;
- **Matplotlib** and **Seaborn**: for data visualization;
- **Streamlit**: for creating an interactive interface;
- **Joblib**: for efficient serialization (saving) and loading of Python objects.
## Dataset
**The dataset used for this project has the following characteristics:**
- **https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset**
- format: `.csv`;
- contains the following key columns: `gender`, `age`, `hypertension`, `stroke`, etc.
This dataset contains 5110 rows with 12 different characteristics:
- **id**: unique identifier;
- **gender**: the patient's gender ("Male", "Female" or "Other");
- **age**: patient's age;
- **hypertension**: the presence of hypertension (0 - no, 1 - yes);
- **heart_disease**: presence of heart disease (0 - no, 1 - yes);
- **ever_married**: marriage status ("No" or "Yes");
- **work_type**: type of work ("children", "Govt_jov", "Never_worked", "Private" or "Self-employed");
- **Residence_type**: type of residence ("Rural" or "Urban");
- **avg_glucose_level**: average blood glucose level;
- **bmi**: body mass index;
- **smoking_status**: smoking status ("formerly smoked", "never smoked", "smokes" or "Unknown");
- **stroke**: whether a stroke has occurred (0 - no, 1 - yes).
**_Correlation analysis_**
# **_Correlation results:_**
❌ **_Positive association with stroke risk_**:
**_age_**: 0.245239 - older people have a higher risk;
**_heart_disease_**: 0.134905 - people with heart disease have a higher risk of stroke;
**_hypertension_**: 0.127891 - people with hypertension have a higher risk of stroke;
**_avg_glucose_level_**: 0.131991 - higher glucose levels are associated with a higher risk of stroke;
**_ever_married_Yes_**: 0.108299 - married people have a slightly higher risk of stroke;
**_smoking_status_formerly smoked_**: 0.064683 - former smokers have a higher risk of stroke;
**_work_type_Self-employed_**: 0.062150 - self-employed persons have a higher risk of stroke;
**_bmi_**: 0.036075 - higher body mass index is associated with higher risk of stroke;
---
❗**Positive but very weak association**:
**_Residence_type_Urban_**: 0.015415 - urban residence;
**_work_type_Private_**: 0.011927 - work in the private sector;
**_gender_Male_**: 0.009081 - male gender;
**_smoking_status_smokes_**: 0.008920 - smokers;
**_work_type_Govt_job_**: 0.002660 - work in the civil service;
---
❎ **_Negative but very weak relationship_**:
**_smoking_status_never smoked_**: -0.004163 - people who have never smoked;
**_gender_Female_**: -0.009081 - female gender;
**_work_type_Never_worked_**: -0.014885 - people who have never worked;
**_Residence_type_Rural_**: -0.015415 - rural residence;
---
✅ **_Negative_relationship_**:
**_smoking_status_Unknown_**: -0.055924 - people with unknown smoking status;
**_work_type_children_**: -0.083888 - children have a lower risk of stroke;
**_ever_married_No_**: -0.108299 - unmarried people have a lower risk of stroke.
# **_Model Comparison_**
| Model | Dataset | Precision | Recall | F1_score | Accuracy |
| ---------------------- | ------- | --------- | -------- | -------- | -------- |
| Logistic Regression | Train | 0.142370 | 0.839196 | 0.243440 | 0.746024 |
| Decision Tree | Train | 0.443207 | 1.000000 | 0.614198 | 0.938830 |
| Random Forest | Train | 0.503856 | 0.984925 | 0.666667 | 0.952043 |
| Gradient Boosting | Train | 0.991736 | 0.603015 | 0.750000 | 0.980426 |
| Balanced Random Forest | Train | 0.208814 | 1.000000 | 0.345486 | 0.815513 |
| Logistic Regression | Test | 0.130282 | 0.740000 | 0.221557 | 0.745597 |
| Decision Tree | Test | 0.141593 | 0.320000 | 0.196319 | 0.871820 |
| Random Forest | Test | 0.171053 | 0.260000 | 0.206349 | 0.902153 |
| Gradient Boosting | Test | 0.222222 | 0.040000 | 0.067797 | 0.946184 |
| Balanced Random Forest | Test | 0.153846 | 0.720000 | 0.253521 | 0.792564 |
**Confusion matrix LogisticRegression**
**Confusion matrix BalancedRandomForestClassifier**
# 💓**Summing up**
**_LogisticRegression_** and **_BalancedRandomForestClassifier_** show the highest sensitivity on the test dataset. However, considering also other metrics such as precision and F1-measure, the BalancedRandomForestClassifier may be a more suitable choice for this task.
If the **_primary goal_** is not to miss patients, i.e. minimize false negatives, then the key metric is **_Recall_**, namely the sensitivity for class 1.
Recall determines the proportion of correctly identified patients among all valid patients, which is critical if we are more concerned about a situation where a sick patient is classified as healthy.
The main metric is Recall for a positive class, i.e. minimizing false negatives, and from the presented results, LogisticRegression demonstrates the highest recall for class 1 - 0.74 on the test data, BalancedRandomForestClassifier - 0.72.
This means that 74% and 72% of sick patients, respectively, were correctly identified, which is critical if a diagnostic error can lead to a patient being classified as healthy.
However, it is important to remember that a high level of memorization can be accompanied by low accuracy - a large number of false positives. In my case, the accuracy for class 1 remains low for LogisticRegression - 0.13 and for BalancedRandomForestClassifier - 0.15.
✅ **_Therefore, if the main goal is not to miss sick patients, then LogisticRegression is the most appropriate model._**
## Run locally
**Clone the repository:**
```
git clone https://github.com/MariiaSam/Stroke-Prediction.git
cd Stroke-Prediction
```
**Set up the virtual environment with Poetry**
Set up project dependencies:
```
poetry install
```
To activate the virtual environment, run the command:
```
poetry shell
```
To add a dependency to a project, run the command:
```
poetry add
```
To pull in existing dependencies:
```
poetry install
```
# Using
Run the Streamlit application with the command:
```
streamlit run app.py
```