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STROKE PREDICTION

Machine Learning tool to predict risk of having stroke.

Repo Github: https://github.com/vnk8071/stroke-prediction

Link blog: https://github.com/vnk8071/stroke-prediction/blob/master/blog_post.md

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Domain Background​

According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. It will be good if we detect and prevent this deadly disease in time, it will bring happiness to the sick and have more time to live.

Problem Statement​

Currently, classification methods using machine learning and deep learning have become popular to assist doctors in diagnosing stroke and providing timely treatment. Here, we use two models of machine learning to predict stroke based on the input parameters like gender, age, various diseases, and smoking status. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important.

Dataset​

The data collect from Kaggle: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset

Solution Statement​

We use 6 models of Machine Learning (Logistic Regression, lightGBM, xgboost, Adaboost, Random Forest and Decision Tree) and compare them with each other.

The output expected: Logistic Regreesion has area under curve (82%) higher than each other.

Install​

Create virtual environment

conda create -n myenv python=3.7
conda activate myenv

Change directory:

cd stroke-prediction/

Install dependencies:

pip install -r requirements.txt

Download and set up data by running

./setup-data.sh

or

bash ./setup-data.sh

Wait about 30 seconds to download data

Usage​

Run terminal and save models

python train.py

Use streamlit to predict stroke

streamlit run streamlit.py

Domain Background​

According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. It will be good if we detect and prevent this deadly disease in time, it will bring happiness to the sick and have more time to live.

Problem Statement​

Currently, classification methods using machine learning and deep learning have become popular to assist doctors in diagnosing stroke and providing timely treatment. Here, we use two models of machine learning to predict stroke based on the input parameters like gender, age, various diseases, and smoking status. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important.

Dataset​

The data collect from Kaggle: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset

Solution Statement​

We use 6 models of Machine Learning (Logistic Regression, lightGBM, xgboost, Adaboost, Random Forest and Decision Tree) and compare them with each other.

The output expected: Logistic Regreesion has area under curve (82%) higher than each other.

Analysis​

Question 1: What is the ratio of stroke patients to non-stroke patients in this dataset and it follows the same ratio in the real world?​

The ratio of stroke patients to non-stroke patients in this dataset is 1:24. It is not the same ratio in the real world. The ratio of stroke patients to non-stroke patients in the real world is 1:6.

stroke_ratio

Question 2: Is there any difference between gender and stroke?​

The relationship between male and female are the same in heart_disease and residence_type. But with hypertension features, the ratio of males is greater than females.

stroke_heart_disease

stroke_hypertension

stroke_residence_type

Question 3: What is the relationship between age and stroke?​

The relationship between age and stroke is positive. The older the age, the higher the risk of stroke.

stroke_age

Correlation​

We can see that the correlation between age and stroke and married is greater than 0.7.

correlation

Conclusion​

  • Dataset is imbalanced in stroke feature.
  • The ratio of stroke patients to non-stroke patients in this dataset is 1:24. It is not the same ratio in the real world. The ratio of stroke patients to non-stroke patients in the real world is 1:6.
  • The relationship between age and stroke is positive. The older the age, the higher the risk of stroke.