/Brainstroke

Brain stroke prediction using machine learning

Primary LanguageJupyter Notebook

Brainstroke Prediction

Kaggle dataset link : https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

Libraries used for dataset processing

  • Numpy
  • Pandas

Libraries used for graphical representation

  • Matplotlib
  • Seaborn

Libraries used for Scaling and Oversampling

  • Sklearn.preprocessing
  • Imblearn

PREPROCESSING

  • Removed the id column – decreasing the dimension – did not add to insights in the data analysis.
df = df.drop(['id'],axis=1)
  • Count for NULL values are checked among the attributes of the dataset
print(df.isna().sum())
  • Only BMI-Attribute had NULL values
  • Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median.
  • Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke

  • The dataset was skewed because there were only few records which had a positive value for stroke-target attribute

  • In the gender attribute, there were 3 types - Male, Female and Other. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension

  • Most of the attributes in the dataset were binary values – converting the numeric bin values into string bin values for dummy encoding.

    • Dummy encoding similar to one-hot encoding – Values in the binary ecoded columns are 1/0 – Additional attributes/columns created.

  • Random oversampling done on the dataset to balance the skew in the target attributes.

    • Boosting the number of records in the minority class – records

EDA - Exploratory Data Analysis

  • Plotted plots of each attribute - Analyse trends if any – plots: pie, histogram.
  • Plotted relation of target attribute to other attributes to find any correlation.
  • Plotted the heatmap – correlation plot between the attributes.
    • Heatmap showed very less correlation between the attribute values.

MODEL BUILDING

  • Creating a train and test split of the oversampled dataset. (80-20)

Applied various Machine learning models for predictive analysis

  1. Decision tree
  2. KNN
  3. XG-Boost
  4. Random forest
  5. Logistic regression

Analysed the results generated using confusion matrix - accuracy, precision, recall and plotting the ROC plot and generating the AUC scores.

Accuracies calculated:

  1. Decision tree : 97.89%
  2. KNN : 97.22%
  3. XG-Boost : 97.48%
  4. Random forest : 99.48%
  5. Logistic regression : 76.34%

Chosen model - RANDOM FOREST

Results were validated using the k fold (20 splits) validation for overfitting

  • Accuracy: 95.01
    For Random Forest