Update README.md

README.md

@@ -37,7 +37,7 @@ A predictive machine learning model was developed that can classify data points
 
 This model can be directly used for disease diagnosis based on binary encoded medical features. By inputting patient symptoms in the form of binary vectors, the model predicts the likely medical condition. Here’s how you can utilize the model:
 
-####Prepare Input Data:
+Prepare Input Data:
 
 Ensure that the input data is formatted as a binary matrix, where each row represents a patient and each column represents a symptom or feature.
 The target variable should be a categorical label representing the medical condition.
@@ -46,7 +46,7 @@ Load the Model:
 Load the trained Random Forest Classifier or SVM Classifier from the repository.
 You can use libraries like joblib or pickle in Python to load the pre-trained model.
 
-####Make Predictions:
+Make Predictions:
 
 Use the loaded model to make predictions on new input data.
 For instance, in Python:
@@ -56,20 +56,11 @@ import joblib
 model = joblib.load('path_to_model.pkl')
 predictions = model.predict(new_input_data)
 
-####Interpret Results:
+Interpret Results:
 
 The model will output the predicted medical condition for each input row.
 These predictions can be used by healthcare professionals to assist in diagnosing patients.
 
-
-Example Usage:
-
-Suppose you have a new patient’s symptom data in binary format:
-python
-Copy code
-new_patient_data = [0, 1, 0, 1, ..., 0]  # Example binary vector for a new patient
-prediction = model.predict([new_patient_data])
-print("Predicted Condition:", prediction[0])
 This model is intended for direct use in clinical decision support systems or healthcare applications where quick 
 and accurate disease diagnosis is critical. It can be integrated into electronic health records (EHR) systems, patient management software, 
 or used as a standalone diagnostic tool.
@@ -101,7 +92,7 @@ Malicious Use:
 
 The model should not be used to intentionally misdiagnose or manipulate medical diagnoses for fraudulent purposes.
 Ensuring ethical use of the model is paramount, and it should only be used to assist in improving patient care.
-###Diagnostic Scope Limitation:
+Diagnostic Scope Limitation:
 
 The model is trained on specific diseases included in the dataset. It may not perform well in diagnosing conditions outside the scope of its training data.
 For diseases not represented in the training data, the model might default to predicting "other," which should be interpreted with caution.