Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Kumar, Deepak; Verma, Chaman; Dahiya, Sanjay; Singh, Pradeep Kumar; Răboaca, Maria Simona; Illés, Zoltán; Bakariya, Brijesh

doi:10.3390/s21196584

Cited by 18 publications

(9 citation statements)

References 60 publications

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“…Similar results were seen in the study conducted by Kumar et. al., [23]. A number of experiments were performed as part of the study.…”

Section: E Results Comparisonmentioning

confidence: 99%

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A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

Mishra¹

2022

j.electron.electromedical.eng.med.inform

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Heart Failure, an ailment in which the heart isn’t functioning as effectively as it should, causing in an insufficient cardiac output. The effectual functioning of the human body is dependent on how well the heart is able to pump oxygenated, and nutrient rich blood to the tissues and cells. Heart failure falls into the category of cardiovascular diseases - the disorders of the heart and blood vessels. One of the leading causes of global deaths resulting in an estimated 17.9 million deaths globally every year. The condition of heart failure results out of structural changes to the cardiac muscles majorly in the left ventricle. The weakened muscles cause the ventricle to lose its ability to contract completely. Since the left ventricle generates the required pressure for blood circulation, any kind of a failure condition results in the reduction of cardiac power output. This study aims to conduct a thorough survival analysis and survival prediction on the data of 299 patients classified into the class III/IV of heart failure and diagnosed with left ventricular systolic dysfunction. Survival analysis involves the study of the effect of a mediation assessed by measuring the number of subjects survived after that mediation over a period of time. The time starting from a distinct point to the occurrence of a certain event, for example death is known as survival time and the corresponding analysis is known as survival analysis. The analysis was performed using the methods of Kaplan-Meier (KM) estimates and Cox Potential Hazard regression. KM plots showed the survival estimates as a function of each clinical feature and how each feature at various levels affect survival over the period of time. Cox regression modelled the hazard of death event around the clinical features used for the study. As a result of the analysis, ejection fraction, serum creatinine, time and age were identified as highly significant and major risk factors in the advanced stages of heart failure. Age and rise in level of serum creatinine have a deleterious effect on the survival chances. Ejection Fraction has a beneficial effect on survival and with a unit increase in the in the EF level the probability of death event decreases by ~5.2%. Higher rate of mortality is observed during the initial days post diagnosis and the hazard gradually decreases if patients have lived for a certain number of days. Hypertension and anemic condition also seem to be high risk factors. Machine learning classification models for survival prediction were built using the most significant variables found from survival analysis. SVM, decision tree, random forest, XGBoost, and LightGBM algorithm were implemented, and all the models seem to perform well enough. However, the availability of more data will make the models more stable and robust. Smart solutions, like this can reduce the risk of heart failure condition by providing accurate prognosis, survival projections, and risk predictions. Technology and data can combine together to address any disparities in treatment, design better care plan, and improve patient health outcomes. Smart health AI solutions would enhance healthcare policies, enable physicians to look beyond the conventional practices, and increase the patient satisfaction levels not only in case of heart failure conditions but healthcare in general.

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“…Similar results were seen in the study conducted by Kumar et. al., [23]. A number of experiments were performed as part of the study.…”

Section: E Results Comparisonmentioning

confidence: 99%

“…Kumar et. al., [23] Most of the study cited above more or less point to similar health risk factors that trigger and effect the heart failure condition. Renal dysfunction (kidney disorder), diabetes mellitus, high blood pressure, anemia, and smoking are cited as the major risk factors that trigger a heart failure condition.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

Mishra¹

2022

j.electron.electromedical.eng.med.inform

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“…SCN and MSCN not only improve efficiency and training, but they also reduce the model size, resulting in better performance with fewer parameters when compared to other sparse coding methods [19]. Hyperparameter tuning is a well-known method for improving the accuracy or performance of any machine learning model [20,21]. The MSCN network consists of SR in reference modules and one adaptive weight module, which are applied to LR images to obtain one HR image.…”

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confidence: 99%

Super Resolution for Noisy Images Using Convolutional Neural Networks

et al. 2022

Self Cite

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The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.

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“…Serum creatinine sensors hold immense significance in the field of healthcare due to their ability to provide precise and real-time measurements of serum creatinine levels [41]. These sensors play a pivotal role in various critical aspects, including the assessment of kidney function, management of kidney diseases, personalized drug dosing, detection of acute kidney injury, post-transplant monitoring, and advancements in kidney research.…”

Section: About the Serum Creatininementioning

confidence: 99%

Multi-physical quantity sensing based on magnetized plasma spherical photonic crystals with evanescent wave

Xu,

Zhu,

Zhang

2023

J. Phys. D: Appl. Phys.

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In this paper, a novel structure of magnetized plasma spherical photonic crystals is proposed that enables the development of sensors based on the evanescent wave principle. This sensor structure integrates a plasma with two isotropic media and applies periodic boundary conditions. Leveraging the principle of the evanescent wave, when the incident light is perpendicular to the interface of a medium with high optical density or thickness and the incident angle exceeds a critical angle, the transfer matrix method is utilized to compute the sharp peaks in the transmission spectrum. Subsequently, sensors produced using these sharp absorption peaks can detect the magnetic induction intensity, plasma frequency, and solution concentration of serum creatinine. The sensitivity and quality factors of these measurements are 8.35×1010 T-1, 3583, 1.5×10-4 (2πc/d)-1, 2970, 3.55, and 22824, respectively. Furthermore, a detection limit of 9.15502×10-6 RIU should be taken into account to ensure the normal operation of serum creatinine detection, satisfying the minimum requirements of biosensing.

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Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Cited by 18 publications

References 60 publications

A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

Super Resolution for Noisy Images Using Convolutional Neural Networks

Multi-physical quantity sensing based on magnetized plasma spherical photonic crystals with evanescent wave

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