HSCAD:Heart Sound Classification for Accurate Diagnosis using Machine Learning and MATLAB

Sinha, Anurag; Kumar, Biresh; Banerjee, P.; Ramish, Md.

doi:10.1109/compe53109.2021.9752199

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…In this table, the correlation hypothesis across different algorithms is analyzed over time (T1-T5) and across various clusters (K1-K8). The 'Distance Measures' columns (1)(2)(3)(4) represent the distances between the nodes within each cluster. 'Fuzzy','Leach','K-Mean', and 'DANA' columns represent the correlation values or probabilities associated with each cluster for the respective algorithms.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

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Section: Simulation and Resultsmentioning

confidence: 99%

“…They are capable of sensing a wide range of phenomena including temperature, humidity, pressure, and motion. WSNs are used in various applications such as environmental monitoring, health care, military, and industrial automation [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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“…The outcome of their experiment indicated that NB performed better than DT in their work, their work also concludes that NB and DT with information gain calculations perform better than other classifiers but surmises this is due to increased number of attributes. This study had a shortcoming of unspecified real experiment and result 3 …”

Section: Related Workmentioning

confidence: 96%

“…Using cardiac abnormality characteristics as a basis, several research have investigated the application of supervised learning classifiers for CVD diagnosis. As an illustration, a research by Sinha et al 3 classified ECG data for the purpose of identifying CVD using SVMs and wavelet transformations. The research demonstrated the potential of this method for CVD detection with an accuracy of 87.4% and an area under the receiver operating characteristic curve (AUC‐ROC) of 0.93.…”

Section: Introductionmentioning

confidence: 99%

CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

Sinha,

Narula,

Pandey

et al. 2024

Health Science Reports

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Background and AimsDiabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature‐fusion strategy employing supervised learning classifiers.MethodsPreprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics.ResultsBased on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes.ConclusionThe recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.

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“…This approach is more suitable for large-scale environments and nonlinear sensor models [129,130]. Graph-based methods [131,132] represent the problem as a factor graph, where the nodes represent the robot poses and map landmarks and the edges represent the constraints between them.…”

Section: Slam (Simultaneous Localization and Mapping)mentioning

confidence: 99%

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Borkar,

Aljrees,

Pandey

et al. 2023

Processes

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Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

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HSCAD:Heart Sound Classification for Accurate Diagnosis using Machine Learning and MATLAB

Cited by 13 publications

References 13 publications

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

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