Page 149 - Emerging Trends and Innovations in Web-Based Applications and Technologies

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 Example: A prediction stating whether a patient is likely  Data Preprocessing: Data was cleaned, transformed, and
to have a particular chronic disease. This structured prepared for analysis.
approach ensures a systematic development and  Model Selection: Machine Learning (ML) and Deep
evaluation process for machine learning models.
Learning (DL) models, including Logistic Regression, SVM,
Random Forest, CNNs, and RNNs, were explored and
evaluated.
 Data Splitting: Data was divided into training, validation,
and testing sets for model development and evaluation.
 Model Evaluation: Model performance was assessed using
metrics like accuracy, precision, recall, and F1-score.
 Output: The final model aims to assist healthcare
professionals in identifying and managing chronic
diseases effectively.
This concise analysis highlights the key steps of your research
process in a clear and impactful manner.
VII. CONCLUSION
In recent years, Sensors, IoT and AI have been widely
deployed in many areas. These emerging technologies are
being deployed in healthcare for the enhancement of HMS.
Here HMS stands for “Healthcare Management System”.
Therefore, researchers are paying close attention to the
deployment of these technologies in healthcare. In this
research, a survey was conducted to identify the application,
challenges, and open research areas of Sensor-AI-based HMS.
Specifically, a unique taxonomy that illustrates the whole
process of Sensor-AI-based HMS is proposed. For
convenience, the whole process is separated into two major
Fig 2. Flow of Chronic Disease Prediction Model areas: Sensors and AI.
V. PERFORMANCE EVALUATION Data collection and transmission are accomplished with
1. Evaluation Metrics: sensors and IoT frameworks, while AI and ML allow
 Accuracy: The accuracy of disease detection or intelligent decision-making in healthcare systems. Various
prediction models.. aspects of this process have been explored throughout this
survey. From the reviewed literature, it was observed that
 Response Time: The time taken by the system or
technology to provide actionable outputs (e.g., real-time Sensors and IoT frameworks have been successfully
monitoring systems). deployed in several HMS. In particular, sensors and AI
technologies have effectively improved HMS operations by
2. Data Sources or environments used for evaluation: enabling efficient and smart diagnosis, supervision, and
 Clinical datasets for chronic diseases: Diabetes, treatment of diseases and ailments. Nonetheless, it was
cardiovascular diseases. observed that despite the successful implementations of
sensors and IoT in HMS, some critical open issues such as
 Real-world patient data: Simulated data if real-world
user acceptance, data synchronization, scalability, and
data access is limited, discuss the data's size, diversity,
interoperability of sensing and IoT devices, data security and
and relevance to the problem.
privacy, and streamlining practices must be addressed.
3. Proposed technologies: VIII. FUTURE SCOPE
 For instance: Show how machine learning algorithms
The future of chronic disease management is poised for
outperform traditional rule-based systems.
transformation through the integration of artificial intelligence
 Demonstration: How wearable devices enable real-time (AI) and predictive analytics, which are reshaping how
monitoring compared to periodic medical check-ups. healthcare systems approach prevention, diagnosis, and
treatment. AI-driven healthcare leverages advanced algorithms
4. Case Studies or Highlight the real-world innovations:
and data analysis techniques to enhance patient care and
 For example: A patient using a wearable device that
optimize outcomes for individuals with chronic conditions
predicts glucose levels and alerts them to take preventive
action. Expansion of the system to support multiple chronic diseases
simultaneously.
 AI-based patient: To prioritize chronic disease cases.
VI. RESULT ANALYSIS Incorporating genomic data into predictive models for highly
The research followed a structured approach: personalized medicine. Exploring quantum computing to
improve the speed and efficiency of AI-based analytics.
 Data Collection: Comprehensive data was gathered, Expanding access to underserved populations through 5G-
including patient demographics, medical history, lifestyle enabled telemedicine systems.
factors, and relevant medical images.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 139