Update README.md

Author: sidd1915

README.md

@@ -1,15 +1,15 @@
-# Myocardial-Infarction-using-ML
+# Myocardial-Infarction-using-ML (Binary Classification)
 This project focuses on the detection of Myocardial infarction (heart attack) using machine learning techniques. We leverage the PTB diagnostic database, a widely used dataset in the field of cardiology.Myocardial infarction, a critical medical condition, requires prompt diagnosis and treatment. Machine learning algorithms offer the potential to aid healthcare professionals in accurately identifying myocardial infarction cases from electrocardiogram (ECG) data.
 
 Dataset taken from Kaggle - https://www.kaggle.com/datasets/shayanfazeli/heartbeat
 
 ## Methodology-
 
 ## Data Preprocessing:
-Adding labels,normalizing the data,handling the imbalance.
+Added labels, normalized the data, handled class imbalance.
 
 ## Feature Extraction: 
-Reducing feature size using PCA.
+Reduced feature size using PCA.
 
 ## Algorithms Used:
 KNN , Random forest , SVM
@@ -18,22 +18,26 @@ KNN , Random forest , SVM
 Training machine learning models, including but not limited to, Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbours (KNN), on the extracted features.
 
 ## Model Evaluation: 
-Evaluating the performance of trained models using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).
+Evaluated the performance of trained models using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).
 
 ## Usage
 
 1.Clone the repository:
-git clone https://github.com/yourusername/myocardial-infarction-detection.git
+git clone https://github.com/sidd1915/myocardial-infarction-using-ML
 
-2.Install the required dependencies:
-pip install -r requirements.txt
+2.Install the required dependencies
 
-3.Explore the Jupyter notebooks in the notebooks/ directory to understand the data preprocessing, feature extraction, model training, and evaluation processes.
+3.Start Jupyter Notebook:
+  jupyter notebook
 
-4.Run the notebooks sequentially to replicate our experiments or adapt the code to your specific requirements.
+4.Open the notebook file (.ipynb) to explore and run the code cells.
 
 ## Results
 Our experiments demonstrate promising results in myocardial infarction detection using machine learning techniques. The models achieve high accuracy and sensitivity, indicating their potential for assisting healthcare professionals in diagnosing myocardial infarction.
+<img width="100%" alt="Confusion Matrices" src="https://github.com/user-attachments/assets/e1e27cde-60a4-497d-af64-7419eba67afc">
+
+<img width="100%" height="500" alt="AUC-ROC curves" src="https://github.com/user-attachments/assets/463ac9de-0f4c-4403-bb5f-c5d68dde4a49">
+
 
 Future Scope - 
 Citation - Kachuee, M., Fazeli, S. and Sarrafzadeh, M., 2018, June. Ecg heartbeat classification: A deep transferable representation. In 2018 IEEE international conference on healthcare informatics (ICHI) (pp. 443-444). IEEE.