Authors: Mr. Chokkakula Chaitanya, Miss. Savarapu Suhasini
Abstract: Heart disease is one of the leading causes of mortality worldwide, making early and accurate diagnosis essential. This study proposes a next-generation heart disease prediction framework using Quantum Machine Learning (QML) and presents a comparative evaluation with traditional machine learning approaches. A clinical heart disease dataset containing attributes such as age, gender, blood pressure, cholesterol level, and heart rate is pre-processed, balanced, and divided into training and testing sets. Traditional algorithms, including Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Decision Tree, K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA), are compared with Quantum Machine Learning models for disease prediction. The models are evaluated using 5-fold cross-validation with accuracy, precision, recall, F1-score, and ROC-AUC as performance metrics. Results show that Logistic Regression and Linear Discriminant Analysis achieve the best performance among classical models, while Quantum Machine Learning demonstrates competitive prediction capability with improved feature representation. The proposed framework highlights the potential of QML as a scalable and intelligent solution for next-generation heart disease prediction and clinical decision support.
