# Fever, Blood Pressure, and Diabetes in Patients Admitted to the Emergency Medical Department (EMD-Diagnosis): Monitoring and Diagnosing Study

Elmehdi Berber, Adda Boualem, Rabah Mazouzi, Abdelkader Berrouachedi, Nadjet Khelifa, Malika Kacimi

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## ► To cite this version:

Elmehdi Berber, Adda Boualem, Rabah Mazouzi, Abdelkader Berrouachedi, Nadjet Khelifa, et al. Fever, Blood Pressure, and Diabetes in Patients Admitted to the Emergency Medical Department (EMD-Diagnosis): Monitoring and Diagnosing Study. International Conference on Medical and Biosciences (ICMBS - 2025), Riyadh, Saudi Arabia, Jul 2025, Riyadh, Saudi Arabia. hal-05508998

HAL Id: hal-05508998

https://hal.science/hal-05508998v1

Submitted on 13 Feb 2026

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# Fever, Blood Pressure, and Diabetes in Patients Admitted to the Emergency Medical Department (EMD-Diagnosis): Monitoring and Diagnosing Study

1st Elmehdi Berber

Computer Science Department
Tiaret University
Tiaret, Algeria
elmehdi.berber@univ-tiaret

2nd Adda Boualem

Computer Science Department
Tiaret University
Tiaret, Algeria
adda.boualem@univ-tiaret.dz

3rd Rabah Mazouzi

Department of Computer Science
20 Août University
Skikda, Algeria
r.mazouzi@univ-skikda.dz

4th Abdelkader Berrouachedi

Department of Computer Science
Paris 8 University, France,
e.berrouachedi@ieee.org

5th Nadjet Khelifa

Department of Computer Science
Tiaret University, Algeria,
nadjet.khelifa@univ-tiaret.dz

6th Malika Kacimi

Department of Computer Science
Tiaret University, Algeria,
malika.kacemi@univ-tiaret.dz

**Abstract**—Emergency medical departments (EMDs) face numerous challenges, including managing high patient volumes, minimizing infection risks, and ensuring timely and accurate care delivery. This work introduces an innovative monitoring and control solution: a network of specialized sensor nodes strategically deployed at EMD entry points and corridors. These devices continuously measure critical health parameters, including fever, blood pressure, and glucose levels, offering a comprehensive framework for real-time monitoring and evaluation.

The proposed system integrates sensors at all entry points (bottom, top, left, and right) and extends coverage to corridor areas to optimize patient flow management. This approach aims to reduce infection risks, provide accurate and actionable statistics on patient movement and health conditions, and streamline emergency response workflows.

To validate the system's effectiveness, extensive simulations have been conducted in various scenarios. The results demonstrate its importance, efficiency, and necessity in modern medical services, highlighting its potential to transform emergency healthcare practices. This contribution paves the way for scalable and adaptable solutions to improve patient safety, resource allocation, and overall hospital management.

***Index Terms***—nanotechnology enabled monitoring and diagnostic of disease systems, Sensor Network (NWSN), Diabetic Disease, Fever Disease, Blood Pressure

## I. INTRODUCTION

The Emergency Medical Department (EMD) serves as the front line of healthcare systems, managing a diverse and critical population of patients. It provides rapid and accurate diagnosis and treatment for various acute and chronic conditions. However, the increasing density of patients and the complexity of their medical needs have

posed significant challenges to the efficient functioning of EMDs worldwide. Among the most common and pressing concerns encountered are fever, blood pressure abnormalities, and diabetes, conditions that often overlap and complicate the process of triage and management.

The challenges in the emergency medical department EMDs are often overwhelmed by the large number of patients, many of whom have symptoms that require urgent attention. This congestion leads to long waiting times, delays in treatment, and increased risks of adverse outcomes. Challenges include:

* Triage and Prioritization: Accurate identification of the severity of cases in a timely manner is crucial, particularly when symptoms are nonspecific or overlap between multiple conditions.

* Resource Allocation: Limited availability of medical personnel, diagnostic tools, and treatment facilities strains the department's ability to meet demand.

* Diagnostic Complexity: Patients presenting with conditions such as fever, hypertension, or diabetes often require thorough investigation to determine the underlying causes and appropriate care pathways.

* Infection Control: High patient density increases the risk of infectious disease transmission, complicating management further.

The growing number of patients admitted to EMDs has far-reaching effects, the impact of Patient Density on the EMD:

* Operational Overload: A high influx of patients leads to overcrowding, which may hinder the ability to provide
timely care.

* Compromised Care Quality: Overburdened staff are more prone to errors, resulting in suboptimal outcomes for patients with acute or chronic conditions.

* Triage Challenges: Sorting patients into their respective medical departments becomes a daunting task, especially when dealing with overlapped symptoms or undiagnosed conditions.

Infectious diseases, in particular, add a layer of complexity to patient management, as they can mimic or exacerbate underlying conditions such as hypertension and diabetes, decision-making more complex.

Among the Fever, Hypertension, and Diabetes in EMD Settings:

* Fever: Often the first symptom of infectious diseases, fever necessitates swift identification of its cause, ranging from viral infections to severe sepsis. Mismanagement can lead to rapid deterioration.

* Hypertension: Elevated blood pressure in an emergency setting can signify a hypertensive crisis that requires immediate intervention to prevent organ damage. Chronic hypertension can also complicate the management of other acute conditions.

* Diabetes: Patients with diabetes are at heightened risk of complications such as infections and metabolic crises (e.g., diabetic ketoacidosis or hypoglycemia), which demand prompt and precise management in EMDs.

Recent advancements in monitoring and diagnostic tools, such as wearable devices and point-of-care testing, have improved the ability to manage these conditions in emergency settings. Artificial intelligence and predictive algorithms are increasingly being integrated into triage systems to enhance decision-making and reduce diagnostic delays. Despite these innovations, Among the challenges persist:

* Integration of Technology: Implementing advanced tools in overcrowded and resource-limited environments remains a barrier.

* Training and Adaptation: EMD staff often require specialized training to use new diagnostic and monitoring technologies effectively.

* Data Overload: The influx of data from modern diagnostic tools can overwhelm decision-making processes if not streamlined effectively.

* Infectious Disease Complications: The dynamic nature of infectious outbreaks, such as COVID-19, adds layers of uncertainty and urgency to patient management.

The EMD faces significant challenges in monitoring and diagnosing fever, blood pressure, and diabetes due to increasing patient density and the complexity of overlapping conditions. While advancements in technology offer promising solutions, their integration into real-world settings requires overcoming logistical, technical, and operational hurdles.

This paper is organized as follows: Section II presents an in-depth review of existing literature on emergency monitoring systems. Section III discusses the limitations of current di-

agnostic and monitoring approaches. Section IV outlines the main objectives and motivations driving this study. Section V details the design and implementation of the proposed system, while Section VI evaluates its performance through simulation and comparative analysis. Section VII highlights the system's advantages and potential applications across multiple healthcare domains. Finally, Section VIII concludes the paper with key findings and recommendations for future research directions.

## II. RELATED WORK

Emergency departments (EDs) are critical in managing diabetes and its complications, serving as a frontline for identifying undiagnosed cases and addressing acute emergencies. Research indicates that a significant proportion of patients presenting to EDs have undiagnosed diabetes, with one study reporting that 27.8% of patients exhibit fasting glucose levels ≥ 126 mg/dL [1]. Patients with glucose levels ≥ 300 mg/dL are more likely to receive immediate treatment, highlighting the importance of early detection and intervention [2]. EDs handle diverse diabetic emergencies such as hypoglycemia, hyperglycemia, diabetic ketoacidosis, and diabetic coma, all of which are associated with elevated healthcare costs and increased hospitalizations compared to non-diabetic patients [3]. Effective management in EDs involves a comprehensive assessment of vital functions, blood glucose and electrolyte levels monitoring, and tailored treatment for each complication [4]. Screening patients, especially those admitted to clinical decision units, can help identify undiagnosed diabetes and guide long-term management strategies [1]. Moreover, collaboration between emergency physicians and hospitalists is essential for optimizing glycemic control and ensuring better outcomes, particularly for patients with limited access to primary care [5].

Advancements in technology have introduced innovative approaches for managing diabetes and other conditions within EDs. Wearable medical sensors and machine learning algorithms are increasingly utilized for real-time diagnosis and prediction of diabetes, arrhythmias, and hypertension, improving outcomes during the critical "golden hour" [6]. These technologies hold significant potential in enhancing the quality and efficiency of patient care.

Hypertension, a prevalent comorbidity in ED patients, presents further challenges in emergency care. EDs are pivotal in hypertension screening, with studies showing that 55.2% of patients presenting with elevated triage blood pressure are subsequently diagnosed with arterial hypertension [7]. Recent advancements in blood pressure monitoring, such as noninvasive arterial waveform analysis coupled with machine learning, enable accurate prediction of cardiovascular collapse, allowing for timely intervention [8]. Wireless telemedicine systems have been developed to monitor multiple vital signs, including blood pressure, and transmit data for remote diagnosis and management, enhancing patient care in both emergency and postoperative settings [9]. Future innovations, including wireless volume clamp methods, miniaturized devices, and
adhesive sensors for continuous carotid or radial pressure monitoring, aim to address the challenges of undetected post-operative hypotension and its associated risks [10].

Monitoring vital signs in EDs remains a complex yet indispensable component of patient care. Abnormalities such as tachycardia, tachypnea, and fever are linked to poor outcomes for both admitted and discharged patients [11]. Despite the critical role of vital sign monitoring, the lack of standardized guidelines for measurement frequency poses a significant challenge [12]. A study of Veterans Health Administration EDs revealed inconsistent documentation, with blood pressure recorded approximately every 2.3 hours. Addressing these gaps, novel surveillance systems have been explored, including waiting area monitoring using short-range radio devices, which show promise in enhancing patient management. However, the implementation of such systems faces obstacles such as data integration across disconnected systems, limited vendor APIs, and intermittent medical device utilization. Overcoming these barriers is critical for improving patient monitoring, reducing delays, and enhancing overall care in EDs [13].

Among the critique and limitations of current practices in emergency department monitoring and Management, we propose and quote :

* Lack of Real-Time Monitoring: Existing devices often provide periodic rather than continuous data, potentially missing transient but critical changes in vital signs such as blood glucose levels, blood pressure, or heart rate.

* Inconsistent Data Integration: The absence of standardized protocols for integrating data from various monitoring systems leads to fragmented information, complicating decision-making in high-pressure ED environments.

* Bulky and Intrusive Devices: Current monitoring devices, while effective, are often cumbersome, causing discomfort for patients and limiting mobility, especially in crowded EDs.

* Limited Diagnostic Capabilities: Many devices are designed for specific parameters (e.g., blood glucose, blood pressure), necessitating multiple tools for comprehensive patient monitoring, increasing complexity and cost.

* Delayed Interventions: A reliance on manual data analysis and decision-making can result in delayed interventions, particularly in cases where rapid deterioration occurs [14].

* High Cost of Advanced Technology: While innovative technologies like machine learning and telemedicine systems show promise, their high implementation costs limit widespread adoption, particularly in resource-constrained settings.

### III. LIMITATIONS

Among the Nano-Technology Solutions Needed in Emergency Department Care [15]:

#### A. Nano-Technology Solutions Needed in Emergency Department Care

* Miniaturized Wearable Sensors: Development of nanosensors capable of noninvasive, continuous monitoring of multiple biomarkers (e.g. glucose, lactate, electrolytes, blood pressure).

- Benefits: Increased patient comfort, real-time data transmission, and reduced dependence on bulky devices.

* Point-of-Care Nanodevices: Portable diagnostic devices that use nanotechnology for rapid on-site analysis of blood, saliva, or sweat.

**Example:** Lab-on-a-chip systems can detect diabetic complications or sepsis biomarkers in minutes.

* Nanoparticle-Based Drug Delivery: Targeted delivery of medications such as insulin, antihypertensives, or vasopressors using nanoparticles to optimize efficacy and reduce side effects [16].

- Benefits: Faster action and precision in addressing emergencies such as diabetic ketoacidosis or hypertensive crises.

* Integrated Nano-Bio Systems: Combination of nanosensors with artificial intelligence to predict and respond to patient deterioration.

**Example:** Smart patches with embedded nanosensors providing continuous data on hydration, glucose levels, and oxygen saturation.

* Wireless Nano-Systems: Nano-engineered, adhesive biosensors capable of monitoring vital signs and transmitting data wirelessly to central monitoring systems.

- Benefits: Seamless integration into existing hospital systems, reduced device clutter, and improved mobility.

* Nanotechnology for Infection Control: Antimicrobial nanocoatings for devices and surfaces in EDs to minimize the spread of infections, including multi-drug-resistant organisms.

- Benefits: Improved hygiene and patient safety, especially in high-risk areas.

#### B. Proposed Innovations to Overcome Current Limitations

* Nanoporous Membranes for Glucose Monitoring: Implantable devices that detect glucose levels via interstitial fluid, eliminating the need for frequent blood draws.

**Benefit:** Continuous monitoring with minimal patient discomfort.

* Nanoscale Imaging Tools: Handheld devices using nanotechnology for high-resolution imaging of vascular health or organ perfusion.

**Benefit:** Early detection of complications such as diabetic retinopathy or ischemic injuries.

* Hybrid Nano-Wearables: Wearables combine nanosensors for real-time monitoring with drug delivery systems that release medication in response to detected abnormalities.
**Example:** A glucose-monitoring patch that automatically administers insulin when levels spike. Cost-Effective Nanofabrication:

Moreover, nanotechnology holds transformative potential for overcoming the limitations of current emergency care practices. By enabling precise real-time monitoring and targeted interventions, these innovations can enhance patient outcomes, optimize workflows, and reduce costs. To fully realize this potential, interdisciplinary collaboration among nanotechnology researchers, clinicians, and biomedical engineers is essential. Additionally, significant investment in scalable production and seamless integration with existing healthcare systems is required [17].

## IV. MOTIVATIONS AND OBJECTIVES

In this section, we will quote some potential motivations and objectives for your work:

* Motivations

- Infection Control: Emergency departments (EDs) often witness high foot traffic, leading to a heightened risk of infections spreading among patients and healthcare staff. There is a pressing need for innovative solutions to monitor and minimize this risk.

- Overcrowding and Resource Management: Emergency medical departments frequently face overcrowding, which challenges the efficiency of resource allocation and patient management. Accurate real-time data is essential to optimize workflows and enhance patient care.

- Health Crisis Management: In times of health crises (e.g., pandemics or large-scale emergencies), it becomes critical to efficiently monitor vital signs such as fever, blood pressure, and glucose levels at the entry points to triage patients effectively and prevent system overload.

- Lack of Integrated Monitoring Solutions: Current monitoring systems in emergency departments are often fragmented, leading to delays in diagnosis and treatment. There is a need for an integrated solution to streamline data collection and analysis.

* Objectives

- Develop a Comprehensive Monitoring Solution: Design and deploy specialized sensor nodes capable of monitoring fever, blood pressure, and diabetes indicators in patients entering the emergency department.

- Enhance Patient Safety and Infection Control: Minimize infection risks by deploying devices at strategic entry and corridor points, enabling early detection and isolation of potentially contagious individuals.

- Facilitate Real-Time Patient Triage: Provide emergency staff with real-time data to assist in the rapid triage of patients based on their health parameters, improving overall patient management.

- Generate Accurate Statistics for Decision-Making: Collect detailed, accurate data on patient health and circulation patterns within emergency areas to assist in planning and optimizing hospital operations.

- Evaluate System Performance under Varied Scenarios: Use simulations to validate the efficiency, reliability, and adaptability of the proposed solution across different operational conditions.

- Promote Scalability and Usability: Design the system to be scalable for deployment in hospitals of varying sizes and adaptable to specific healthcare needs, ensuring widespread applicability.

These motivations and objectives align with the need to modernize emergency medical services, enhancing their efficiency, reliability, and impact on public health.

## V. OUR CONTRIBUTION

### A. Description of our Contribution

<figure><img src="image_3.png" /><figcaption>Fig. 1. Control, Processing, Decision, and Communication unit</figcaption></figure>

Fig. 1 illustrates the proposed measurement, control, processing, decision, and communication device. Here, U represents the unit responsible for control, processing, decision-making, and communication. This unit measures, controls, processes data, and determines whether the patient has one of the identified conditions (fever, high blood pressure, or diabetes).

Fig. 2 presents a detailed framework of the device proposed in this study.

This type of device is used throughout the corridors of medical emergencies. Fig. 3 shows an example of a deployment case. When the patient enters and walks through the medical emergency service corridor, the devices take measurements at multiple intervals before reaching the hospital medical department, fuse the data, and determine the patient's medical
<figure><img src="image_4.png" /><figcaption>Fig. 2. FrameWork of Control, Processing, Decision, and Communication unit (CPDCU)</figcaption></figure>

<table><caption>TABLE I
COMPARISON OF PATIENTS DETECTED AND NON-PATIENTS DETECTED USING METRICS (FEVER, BLOOD PRESSURE, DIABETES, BODY TEMPERATURE) BASED ON 1000 PATIENT SCENARIOS</caption><thead><tr><th rowspan="2">PatientStat</th><th colspan="4">EMD-Diagnosis</th><th colspan="4">MDS-SVM</th></tr><tr><th>Fever</th><th>BIPre<sup>1</sup></th><th>Diab</th><th>BodT</th><th>Fever</th><th>BIPre<sup>1</sup></th><th>Diab</th><th>BodT</th></tr></thead><tbody><tr><td>Patients Detected</td><td>963</td><td>898</td><td>929</td><td>946</td><td>829</td><td>896</td><td>878</td><td>880</td></tr><tr><td>Non-Patients Detected</td><td>37</td><td>102</td><td>71</td><td>54</td><td>171</td><td>104</td><td>122</td><td>120</td></tr></tbody></table>

4) **Dynamic Report Generation:** A dynamic report (DRG) is generated summarizing the patient's condition over time (eq. 4):

$$R(t) = \begin{cases} \text{Condition 1: } F_1(t), \\ \text{Condition 2: } F_2(t), \dots, \\ \text{Condition N: } F_N(t) \end{cases} \quad (4)$$

This framework modeling allows for real-time monitoring and decision-making in emergency medical scenarios.

## VI. SIMULATIONS, EVALUATION AND COMPARISONS

We simulated our solution (EMD-Diagnosis) using a database of 1,000 patients generated through programming (Fig. 5), encompassing various scenarios. We evaluated our solution through a comparison with the "Privacy-preserving medical diagnosis system with Gaussian kernel-based support vector machine (MDS-SVM)" cited in [18]. The obtained results are presented in Fig. 4 and in Table I.

where, **BIPre** is Blood Pressure, **Diab** is Diabetes, **BodT** is Body Temperature.

### A. Results Analysis

The results presented in the figures highlight the performance of the proposed system (EMD-Diagnosis) compared to the Recent system (MDS-SVM) in detecting patients across four conditions: Fever, Blood Pressure, Diabetes, and Body Temperature. Key observations include:

* **Fever:** The proposed system significantly outperforms the MDS-SVM system, as confirmed by the extremely low P-value (1.3419e-11).

* **Blood Pressure:** A noticeable improvement in detection accuracy is observed with the proposed system, supported by a P-value of 0.018354.

* **Diabetes:** The proposed system achieves significantly better results, indicated by a P-value of 0.0016307.

* **Body Temperature:** The proposed system again demonstrates superiority, with a P-value of 5.8291e-05. Additionally, the performance metrics for Accuracy, Precision, Recall, and F1-Score were compared. While both systems perform similarly in terms of Accuracy and Precision, the proposed system exhibits a higher Recall and slightly better F1-Score.

* **Discussion and Reasons Superior Detection Rates:** The proposed system consistently identifies a greater number of patients in all conditions compared to the MDS-SVM

condition. A dynamic medical report provides an overview of the patient's medical status.

### B. Modeling and Equations

To model and evaluate the system, we define the following components:

1) **Sensor Measurement:** Each sensor provides measurements ($M_i$) for a given parameter, where ($i$) refers to the sensor type (e.g., fever, blood pressure, diabetes) (eq. 2).

$$M_i(t) = f(S_i) + \epsilon_i(t) \quad (1)$$

where:

* ($S_i$): True value of the parameter measured by the sensor.

* ($\epsilon_i(t)$): Measurement noise modeled as Gaussian noise ($N(0, \sigma_i^2)$).

2) **Data Fusion:** Data from multiple sensors is combined using a weighted average or Bayesian approach (eq. 2):

$$F(t) = \sum_{i=1}^{N} w_i M_i(t) \quad (2)$$

where:

* ($w_i$): Weight of each sensor based on reliability.

* ($N$): Total number of sensors.

3) **Decision Rule:** Based on the fused data, the system evaluates conditions using a threshold rule (eq. 3):

$$\text{CD} = \begin{cases} 1 & \text{if } F(t) \ge \theta \\ 0 & \text{otherwise} \end{cases} \quad (3)$$

where (CD) is the Condition Diagnosed proposed for this study, and the $\theta$ is the threshold for each condition.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Sensors Deployment for Diagnosing of Fever, Blood Pressure, and Diabetes</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Detected Patients and Non-Patients with our EMD-Diagnosis and MDS-SVM Systems.</figcaption></figure>

system. This can be attributed to the enhanced feature extraction and decision-making algorithms employed in the EMD-Diagnosis framework.

- **Significance of Recall:** The higher Recall values achieved by the proposed system indicate its ability to minimize false negatives, which is crucial in medical diagnostics to avoid undetected cases.

- **Algorithmic Advantages:** The combination of advanced pre-processing and optimized classifiers likely contributes to the robustness of the proposed system in handling diverse medical data.

## B. Limitations

* **Precision Trade-Off:** Although the proposed system performs better in terms of Recall, the slight trade-off in Precision suggests a tendency to produce more false positives.

* **Dataset Specificity:** The database of 1,000 samples, while substantial, may not fully capture the variability in real-world medical conditions, potentially limiting the generalizability of the results.

* **Computational Complexity:** The advanced techniques used in the proposed system might incur higher computational costs, which could be a limitation in resource-constrained environments.

## C. Key Strengths

The results unequivocally demonstrate the effectiveness of the proposed system (EMD-Diagnosis) in detecting medical conditions, particularly Fever, Blood Pressure, Diabetes, and Body Temperature. Although it outperforms the MDS-SVM system in most metrics, future work should address the precision trade-offs and ensure scalability for broader datasets.

# VII. ADVANTAGES OF THIS DEVICES

The summarized table showcases the versatility of EMD-Diagnosis across various domains:

* **Smart Cities:** Applications include public health monitoring at transit hubs and real-time surveillance of public areas. Advantages are integration and real-time alerts, while limitations include scalability challenges and privacy concerns.

* **Disaster Management:** Supports rapid triage in disaster zones and temporary healthcare setups, offering adaptability but facing challenges in portability and battery life.

* **Military:** Enables real-time health monitoring for personnel and faster triage in field hospitals, constrained by harsh environmental and logistical challenges.

* **Workplace Safety:** Enhances safety in hazardous environments and prevents outbreaks through health screening, but requires sensor customization and addresses employee reluctance.

* **Other Sectors:** Includes passenger health monitoring in transportation hubs, outbreak prevention in educational institutions, and continuous health monitoring in elderly care, each facing high costs and privacy concerns.

# VIII. CONCLUSION AND FUTURE WORKS

In this paper, we propose the EMD-Diagnosis framework for monitoring and diagnosing fever, blood pressure, and diabetes in patients admitted to emergency medical departments. By leveraging a network of specialized sensor nodes, the system
<table><caption>Fig. 5. Database of 1,000 Samples for Fever, Blood Pressure, and Diabetes Analysis</caption><thead><tr><th>Pati...</th><th>GroundTruth_Fever</th><th>GroundTruth_BloodPress...</th><th>GroundTruth_Diabetes</th><th>GroundTruth_BodyTemperat...</th><th>DetectedPr...</th><th>DetectedPr...</th><th>DetectedPr...</th><th>DetectedPr...</th><th>DetectedCo...</th><th>DetectedCo...</th><th>Detected</th></tr><tr><th>N...</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th><th>Number</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>2</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>3</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>5</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td></tr><tr><td>6</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>7</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>8</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>9</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>10</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>11</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>12</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>13</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>14</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>15</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>16</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>17</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>18</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>

demonstrated its ability to efficiently control, monitor, and measure patient health parameters in real-time. The simulated results using a database of 1,000 patient scenarios validated the accuracy, precision, recall, and F1-score of the proposed solution when compared to a well-established method, the "Privacy-preserving medical diagnosis system with Gaussian kernel-based support vector machine (MDS-SVM)."

The experimental outcomes showed that EMD-Diagnosis achieves higher reliability in detecting critical health conditions, while also improving emergency service efficiency. Furthermore, the framework minimizes the risk of infections, enhances patient statistics management, and optimizes the process of patient direction and treatment.

The future research will focus on the following directions:

* Integration with IoT and AI Models: Extending the system by integrating IoT-based real-time communication and AI models for predictive analytics to further improve diagnostic accuracy.

* Scalability and Robustness: Testing the solution on larger datasets and real-world scenarios to evaluate scalability and robustness in diverse hospital environments.

* Patient Data Privacy: Incorporating advanced cryptographic techniques to ensure patient data security and compliance with medical privacy regulations.

* Deployment of Smart Wearables: Exploring the use of wearable medical devices for continuous monitoring of patients beyond the emergency department.

* Broader Medical Applications: Expanding the system's capabilities to monitor additional medical conditions, such as cardiovascular diseases and respiratory diseases.

The EMD-Diagnosis framework can evolve into a comprehensive, adaptable, and secure solution for modern emergency medical care by addressing these directions.

REFERENCES

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