Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression

Srinivasan, Kathiravan; Mahendran, N.; Vincent, Durai Raj; Chang, Chuan Yu; Syed-Abdul, Shabbir

doi:10.3390/electronics9040647

Cited by 17 publications

(11 citation statements)

References 26 publications

(29 reference statements)

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“…However, most of this use of IoT fog and edge computing was done in a field other than healthcare. In healthcare, fog and edge computing can create latency benchmarks and a reliable monitoring system for real-time applications [92][93][94].…”

Section: Fog/edgementioning

confidence: 99%

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

et al. 2021

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IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

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Section: Fog/edgementioning

confidence: 99%

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

et al. 2021

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“…Reference [16] advised a patient's electrocardiogram (ECG) monitoring collecting and displaying the ECG data in real-time. References [18][19][20][21][22] have suggested various methods for monitoring the patient's medical condition. Psychological parameters play a vital role in a patient's recovery.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

Jayalakshmi¹,

Garg²,

Maharajan³

et al. 2021

Computers, Materials &Amp; Continua

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In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID'19 patient's conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID'19 patients and quarantined people. It identi es the patient's medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identi cation. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID'19 patients based on the current context.

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“…Artificial intelligence (AI) has been successful in many fields and facilitates our daily life in various ways (10)(11)(12)(13)(14)(15)(16)(17). The reproduction rate prediction is crucial in successfully establishing public healthcare in the battle against COVID-19.…”

Section: Motivationmentioning

confidence: 99%

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Kaliappan

Srinivasan

Qaisar

et al. 2021

Front. Public Health

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This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

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Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression

Cited by 17 publications

References 26 publications

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

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