Quantum SVM for Anemia Prediction This project uses a Quantum Support Vector Machine (QSVM) to predict anemia based on blood test data. It was built with the goal of combining quantum computing and machine learning to tackle real-world health problems in a unique way. Anemia is a common condition, and early detection is important. In this project, I used just two features from a blood test — Hemoglobin and MCHC — to predict whether someone is anemic. It’s a minimal but effective dataset. " The twist? Instead of only using classical models, I used Qiskit to apply a quantum kernel with a Support Vector Machine — something that pushes beyond traditional ML. " Technologies Used: Python Qiskit (Quantum kernel, Aer simulator) Scikit-learn (for data preprocessing and classical SVM) Imbalanced-learn (SMOTE for handling class imbalance) Pandas, NumPy Accuracy: Quantum SVM Accuracy: ~93.7% Classical SVM Accuracy: ~75% The quantum approach showed a significant improvement after optimizing feature maps and balancing the dataset. Open the notebook in Google Colab or Jupyter Install dependencies: python Copy Edit !pip install qiskit==0.43.0 !pip install qiskit-machine-learning==0.5.0 !pip install imbalanced-learn Upload the anemia.csv file Run each cell step-by-step ""I built this as part of a hackathon to explore how quantum computing can create real impact, especially in health prediction systems for underserved regions. It’s a small step, but one that blends technology with purpose."" personal detials{Naveed Ahamed B.Tech AI & Data Science (3rd year) GitHub: navvwd Email: [email protected]} Assalamu Alaikum.