Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis

Abstract

The earlier prediction of heart diseases and appropriate treatment are important for preventing cardiac failure complications and reducing the mortality rate. The traditional prediction and classification approaches have resulted in a minimum rate of prediction accuracy and hence to overcome the pitfalls in existing systems, the present research is aimed to perform the prediction of heart diseases with quantum learning. When quantum learning is employed in ML (Machine Learning) and DL (Deep Learning) algorithms, complex data can be performed efficiently with less time and a higher accuracy rate. Moreover, the proposed ML and DL algorithms possess the ability to adapt to predictions with alterations in the dataset integrated with quantum computing that provides robustness in the earlier detection of chronic diseases. The Cleveland heart disease dataset is being pre-processed for the checking of missing values to avoid incorrect predictions and also for improvising the rate of accuracy. Further, SVM (Support Vector Machine), DT (Decision Tree) and RF (Random Forest) are used to perform classification. Finally, disease prediction is performed with the proposed instance-based quantum ML and DL method in which the number of qubits is computed with respect to features and optimized with instance-based learning. Additionally, a comparative assessment is provided for quantifying the differences between the standard classification algorithms with quantum-based learning in order to determine the significance of quantum-based detection in heart failure. From the results, the accuracy of the proposed system using instance-based quantum DL and instance-based quantum ML is found to be 98% and 83.6% respectively.

Predictive Analytics in Heart Failure Prediction: A Systematic Review

“…Data Cleaning [7], [13], [19], [20], [23], [26], [28], [32], [33], [34], [35], [36], [39], [40], [42], [41], [45], [46], [47], [54], [55], [60], [64], [65], [66], [68], [69] Mean imputation, predictive mean matching, median imputation, random forest imputation, kNN imputation, XGBoost imputation, and missForest…”

“…In this study the proposed model with a clusterization approach resulted in outstanding predictive performance with high CPU utilization and low processing time. In another study, [28], implementing quantum computing in machine learning and deep learning algorithms resulted in better predictive performance than conventional machine learning algorithms. The study solved the complex dimension and size problem of the UCI Heart Disease dataset by using quantum computing to build predictive models, then compared their classification performance with other machine learning algorithms.…”