Smart Monitoring System for Chronic Kidney Disease Patients based on Fuzzy Logic and IoT

Maniam, Govind; Sampe, Jahariah; Jaafar, Rosmina; Ibrahim, Mohd Faisal

doi:10.14569/ijacsa.2022.0130238

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…e function of LR is to predict the presence or absence of CKD. Equation (3) demonstrates that the LR model is mostly reliant upon that linear regression technique:…”

Section: Rl-based Classification Modelmentioning

confidence: 99%

“…Combining IoT with Cloud Computing (CC) is beneficial when it comes to the creation of innovative approaches. An observation model has been built underneath the IoT integration and CC to monitor the state of patients and successfully gather the information, even in distant locations of greater assistance to medical professionals [ 3 ]. This model was created to watch the patient's condition.…”

Section: Introductionmentioning

confidence: 99%

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Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Rathee¹,

Vinmathi²,

Priya³

et al. 2022

Computational Intelligence and Neuroscience

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It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model’s classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model’s efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system’s hospital capacity and monitoring of a significant number of patients with a concentrated average delay.

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“…e function of LR is to predict the presence or absence of CKD. Equation (3) demonstrates that the LR model is mostly reliant upon that linear regression technique:…”

Section: Rl-based Classification Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Rathee¹,

Vinmathi²,

Priya³

et al. 2022

Computational Intelligence and Neuroscience

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“…Coronavirus disease (COVID - 19) is a dangerous infectious disease also knows as SARS-CoV-2. COVID-19 was first discovered in the city of Wuhan in December 2019 [1].…”

Section: Introductionmentioning

confidence: 99%

Bio-FET Sensor Interface Module for COVID-19 Monitoring Using IoT

et al. 2022

Self Cite

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Rapid transmission of the coronavirus disease via droplets and particles has led to a global pandemic. Expeditious detection of SARS-Cov-2 RNA in the environment is attainable by using Bio-FET sensors. This work proposes a Bio-FET sensor interface module with IoT implementation to amplify signals from a Bio-FET for SARS-Cov-2 detection and monitoring. The sensor interface module was programmed to read the signals using a micro-controller and process information to determine the presence of SARS-Cov-2. The proposed Bio-FET sensor interface module was also set to transmit data to the Cloud via W-Fi to be stored and displayed on a dashboard. The prototype Bio-FET sensor interface module was simulated in PSpice for signal amplification, and hardware implementation has been done by using low-cost components for data transmission to the Cloud. The hardware consists of an AD620 instrumentation amplifier module, voltage sensor module, Neo-6m GPS sensor module, an OLED display, and an ESP8266-32 bit micro-controller. The results of both the simulation and the hardware implementation are similar. The emulated negative and positive Bio-FET signal outputs were successfully amplified from 15.9mV and 45.8mV to 1.59V and 4.58V, respectively, using an AD620 instrumentation amplifier. The gathered location, time, date, output voltage, and SARS-Cov-2 presence results were successfully stored and displayed on the Cloud dashboard.

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“…Radio Frequency Identification (RFID) is one of the integration applications that work with the IoT that is widely employed in the biomedical and healthcare industries. With the IoT, we can monitor a patient's health such and observing his severe or chronic disease [1] and tracking medical members and properties through smart application devices by using Wireless Sensor Network (WSN) nodes [2,3] or RFID tags that are connected to the server. RFID is a system that aims to exchange identification information or data from the tag to the RFID reader through an RF transceiver antenna [4] to carry out specific applications based on the data stored in the tag [5].…”

Section: Introductionmentioning

confidence: 99%

A Fast Digital Phase Frequency Detector with Preset Word Frequency Searching in ADPLL for a UHF RFID Reader

Ishak

Sampe

Nayan

et al. 2022

Eng. Technol. Appl. Sci. Res.

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An All-Digital Phase-Locked Loop (ADPLL) is an architecture that is widely employed in the communication system due to the advancement of the Complementary Metal-Oxide-Semiconductor (CMOS) technology process. A 2.4GHz Radio Frequency Identification (RFID) system needs a frequency synthesizer in the local oscillator architecture of the transceiver to generate a stable frequency tuning range Therefore, in this paper, a Digital Phase-Frequency Detector (DPFD) is designed to achieve the phase and frequency acquisition in the ADPLL system. The proposed DPFD is divided into two main parts, the first is the Phase Detector (PD) and the second is the Frequency Detector (FD). The PD has managed to detect the presence of the phase difference by recognizing two different input signals. The FD, on the other hand, is capable to detect the higher frequency by identifying the output signals from the PD in digital formation. In addition, a control unit module is developed to control and adjust the Preset Word (PW) for the system by using a binary search scheme. Comparison results show that the final value of the PW from the simulation is the same as from the manual calculation (theoretical values). The digital PFD and the PW control modules are designed and simulated by using Verilog HDL code. These two designed modules will be integrated into the targeted ADPLL to achieve fast locking performance and ultra-low power for Ultra-High Frequency (UHF) RFID applications.

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Smart Monitoring System for Chronic Kidney Disease Patients based on Fuzzy Logic and IoT

Cited by 9 publications

References 20 publications

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Bio-FET Sensor Interface Module for COVID-19 Monitoring Using IoT

A Fast Digital Phase Frequency Detector with Preset Word Frequency Searching in ADPLL for a UHF RFID Reader

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