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

Gupta, Himanshu; Varshney, Hirdesh; Sharma, Tarun Kumar; Pachauri, Nikhil; Verma, Om Prakash

doi:10.1007/s40747-021-00398-7

Cited by 81 publications

(43 citation statements)

References 35 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a particular adoption in this frame of reference: in QML models, there are two most important things are state preparation techniques [91], [154] and feature map [88], [89], [95], [146]- [148], different data encoding methods, such as feature extraction methods [81], [95], [99], [141], [150], and data encoding methods, like basis encoding, amplitude encoding [88], [154], and other encodings. The state preparation method is to encode classical data into the quantum state, and the feature map is converted 2D to higher dimensions using Hilbert space [88], [89], [95], [146]- [148].…”

Section: Discussionmentioning

confidence: 99%

“…They concluded that the classical system exceeded the quantum system by a little margin. With the PIMA diabetes dataset, H. Gupta et al [95] employed the exploratory data analysis (EDA) and preprocessing technique for data scaling and applied it to the VQC, root mean square propagation (RMSprop), and DL models for classification. They used back-propagation and the VQC approach to assess RMSprop in that research.…”

Section: Medical Healthcare Recordsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

2022

View full text Add to dashboard Cite

Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning, become a quite challenging field in case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Medical Healthcare Recordsmentioning

confidence: 99%

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

2022

View full text Add to dashboard Cite

show abstract

“…In the present work, to detect and omit the outliers, quartiles have been employed and it has been mathematically presented as in Eq. ( 1 ) [ 23 ]. where, k symbolizes the presence of the feature vector in m -dimensional feature space ( ).…”

Section: Methodsmentioning

confidence: 99%

“…In this work, most popular ML models such as Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Light Grading Boosting Machine (LGBM), and Multilayer feed-forward Perceptron (MLP) have been employed for this purpose. These models have been selected because of their tremendous performance in various tasks [ 23 , 24 ]. Logistic Regression LR has been considered as one of the most simple yet, effective ML models.…”

Section: Methodsmentioning

confidence: 99%

Vaccine hesitancy in the post-vaccination COVID-19 era: a machine learning and statistical analysis driven study

Gupta

Verma

2022

Evol. Intel.

Self Cite

View full text Add to dashboard Cite

Background The COVID-19 pandemic has badly affected people of all ages globally. Therefore, its vaccine has been developed and made available for public use in unprecedented times. However, because of various levels of hesitancy, it did not have general acceptance. The main objective of this work is to identify the risk associated with the COVID-19 vaccines by developing a prognosis tool that will help in enhancing its acceptability and therefore, reducing the lethality of SARS-CoV-2. Methods: The obtained raw VAERS dataset has three files indicating medical history, vaccination status, and post vaccination symptoms respectively with more than 354 thousand samples. After pre-processing, this raw dataset has been merged into one with 85 different attributes however, the whole analysis has been subdivided into three scenarios ((i) medical history (ii) reaction of vaccination (iii) combination of both). Further, Machine Learning (ML) models which includes Linear Regression (LR), Random Forest (RF), Naive Bayes (NB), Light Gradient Boosting Algorithm (LGBM), and Multilayer feed-forward perceptron (MLP) have been employed to predict the most probable outcome and their performance has been evaluated based on various performance parameters. Also, the chi-square (statistical), LR, RF, and LGBM have been utilized to estimate the most probable attribute in the dataset that resulted in death, hospitalization, and COVID-19. Results: For the above mentioned scenarios, all the models estimates different attributes (such as cardiac arrest, Cancer, Hyperlipidemia, Kidney Disease, Diabetes, Atrial Fibrillation, Dementia, Thyroid, etc.) for death, hospitalization, and COVID-19 even after vaccination. Further, for prediction, LGBM outperforms all the other developed models in most of the scenarios whereas, LR, RF, NB, and MLP perform satisfactorily in patches. Conclusion: The male population in the age group of 50–70 has been found most susceptible to this virus. Also, people with existing serious illnesses have been found most vulnerable. Therefore, they must be vaccinated in close observations. Generally, no serious adverse effect of the vaccine has been observed therefore, people must vaccinate themselves without any hesitation at the earliest. Also, the model developed using LGBM establishes its supremacy over all the other prediction models. Therefore, it can be very helpful for the policymakers in administrating and prioritizing the population for the different vaccination programs.

show abstract

“…Some deep learning approach had been applied into diabetes dataset to get more suitable results for detecting diabetes [ 27 , 34 ]. For example, Gupta et al [ 16 ] used deep learning (DL) and quantum machine learning (QML) to detect diabetes where DL outperformed related QML algorithms.…”

Section: Related Workmentioning

confidence: 99%

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Howlader

Satu

Awal

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

show abstract

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

Cited by 81 publications

References 35 publications

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

Vaccine hesitancy in the post-vaccination COVID-19 era: a machine learning and statistical analysis driven study

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Contact Info

Product

Resources

About