Mobile-Based Prediction Framework for Disease Detection Using Hybrid Data Mining Approach

Rathi, Megha; Gupta, Ayush

doi:10.1007/978-981-15-4992-2_49

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Unlike conventional algorithms, which mainly focus on design, quantum algorithms are versatile and can effectively solve different problems. Adaptive learning autonomously finds the possible set of behaviors and patterns to solve a complex problem [18].…”

Section: Process Of Traditional Machine Learning and Quantum-enhanced Machine Learningmentioning

confidence: 99%

“…Quantum learning has proved to be a promising method in biomedical imaging [59] (v) Complex optimization problems: artificial neural network has resulted to be a precise diagnostic approach in traditional machine learning, which is optimized by varying the specifications of network's framework. These methods of optimization are convenient for quantum computing, where the propensity of "quantum tunnelling" fosters optimization problems to be computed quickly [11,12,18,34,35] The two most important applications are quantumenhanced sampling and discrete optimization. Quantum- 13 Wireless Communications and Mobile Computing enhanced sampling is the process of extracting a slice of a probability distribution from a quantum system, and in finance, discrete optimization is used to maximize the yield of a group of financial properties, which is an optimization challenge, where as in most cases, shallow learning approaches are inaccurate.…”

Section: Future Research Perspective Of Qml In the Healthcarementioning

confidence: 99%

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Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Kumar

Koul

Sisodia

et al. 2021

Wireless Communications and Mobile Computing

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Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

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Section: Process Of Traditional Machine Learning and Quantum-enhanced Machine Learningmentioning

confidence: 99%

Section: Future Research Perspective Of Qml In the Healthcarementioning

confidence: 99%

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Kumar

Koul

Sisodia

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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Predictive the Heart Disease Using the Weighted Gain Decision Tree Algorthim

Saraswat

Singh

2022

Global Journal for Research Analysis

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Data mining techniques have been mostly used in medical area for prediction and diagnosis of various diseases. These techniques discover the hidden pattern and relationship in medical data and therefore have been very important in designing clinical support. Now a day's data mining techniques are widely used in diagnosis of heart disease because of increasing death rate worldwide. The reason of this may be the complex and expensive tests conducted in labs to predict the heart disease. Systems based on these risk factors not only benet healthcare professionals, but warn them of the potential presence of heart disease even before a patient is admitted to the hospital or undergoes an expensive medical examination. This in order to reduce the risk of this disease a better approach would to identify risk factor the result in heart disease. This study is an effort in this direction. This approach to predict the heart disease in early stage is developed in present study by analyzing risk factors. This technique developed weighted gain decision tree predicts the risk of heart disease with an accuracy of 90%.

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Mobile-Based Prediction Framework for Disease Detection Using Hybrid Data Mining Approach

Cited by 2 publications

References 7 publications

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Predictive the Heart Disease Using the Weighted Gain Decision Tree Algorthim

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