🧠 Brain Stroke Detection with 6 DOF Robotic Simulation

This project aims to detect the risk of brain stroke using machine learning techniques and simulate a 6 DOF (Degrees of Freedom) robotic arm that assists in medical diagnostics. The robot is enhanced with concepts of Robotic CALM (Cooperative and Autonomous Learning Manipulator), Focus, and Flair to perform precision-based and adaptive tasks in a simulated healthcare environment.

🧪 Project Overview

• ✅ Brain Stroke Prediction using ML models on patient health data.
• 🤖 6 DOF Robotic Arm Simulation to mimic medical equipment alignment tasks.
• 🧠 Robotic CALM: Robot learns from environment to assist doctors more efficiently.
• 🎯 Focus: Precision in targeting brain regions or patient data.
• ✨ Flair: Adaptive and smooth robot motion for enhanced user experience.

🧬 Dataset

• File: brain_stroke.csv
• Source: Kaggle or custom
• Features include:
  • Age, gender, hypertension, heart disease
  • Smoking status, glucose level, C-reactive protein
  • Cholesterol level, physical activity level, alcohol consumption
  • Family history

🧠 Machine Learning

• Preprocessing:

  • Handling missing values
  • Encoding categorical data
  • Feature scaling and normalization

• Models Used:

  • Logistic Regression
  • Random Forest
  • Support Vector Machine (SVM)

• Evaluation Metrics:

  • Accuracy
  • Precision, Recall, F1-score
  • ROC-AUC

🤖 Robotic Simulation

• 6 DOF Robotic Arm:

  • Simulated using PyBullet or Gazebo
  • Performs equipment alignment or brain scan assist tasks
  • Controlled using inverse kinematics for precision

• Robotic CALM:

  • Learns from past movements and scan data to improve accuracy
  • Reacts autonomously to doctor/patient interaction or scan feedback

• Focus:

  • Precisely targets areas on brain scans or device alignment zones

• Flair:

  • Introduces smooth motion transitions and adaptive learning features

🔧 Technologies Used

Category	Tools/Libraries
Language	Python
ML Libraries	scikit-learn, pandas, NumPy
Visualization	Matplotlib, Seaborn
Simulation	PyBullet (or Gazebo/ROS)
Deployment	Flask (for ML model deployment)

🚀 Screenshots

1. Clone the Repository

git clone https://github.com/your-username/brain-stroke-detection.git
cd brain-stroke-detection