Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

Complex & Intelligent Systems - 2022
Himanshu Gupta1, Hirdesh Varshney2, Tarun Kumar Sharma3, Nikhil Pachauri4, Om Prakash Verma1
1Department of Instrumentation and Control Engineering, Dr. B R Ambedkar National Institute of Technology Jalandhar, Jalandhar, India
2Department of Computer Science and Engineering, Babu Banarsi Das University, Lucknow, India
3Department of Computer Science and Engineering, Shobhit University Gangoh, Saharanpur, India
4School of Electrical and Electronics, SASTRA Deemed to be University, Thanjavur, Tamilnadu, India

Tóm tắt

Abstract Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

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