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predicting disease progression, personalizing treatment chronic disease management by examining reviews of
recommendations, and identifying high-risk individuals. previous studies on chronic disease prediction using
machine learning and deep learning, highlighting their
 Mobile Health (mHealth) Interventions: Studies have
methods, technologies, and key findings.
evaluated the impact of mobile apps, text message
reminders, and other mHealth interventions on patient III. PROPOSED WORK
engagement, self-management skills, and health Our Research paper proposes a comprehensive system
outcomes. integrating emerging technologies for better management of
chronic diseases:
 Social Determinants of Health: Research has examined
the social, economic, and environmental factors that 1. Development of a unified health management
contribute to chronic disease disparities and explored platform combining wearables, telemedicine, and AI-
interventions to address these challenges based analytic Integration of predictive AI models to
foresee disease progression and optimize treatment
 Patient-Centered Care Models: Studies have investigated plans.
the effectiveness of patient-centered care models that
prioritize patient autonomy, shared decision- making, 2. Application of IoT-enabled devices to collect real-time
and personalized care plan. patient data for early diagnosis and intervention.
This section provides a concise overview of relevant 3. Inclusion of behavioral modification tools, such as
research areas, setting the stage for your own investigation gamified mobile applications, to improve adherence to
into innovative approaches and emerging technologies for treatment plans

Fig 1. Workflow of Chronic Data using ML Algorithm
IV. PROPOSED RESEARCH MODEL  Approach: Analyze the problem type (e.g., classification
This process flow represents the structured steps involved in for disease prediction).Test algorithms like Random
building a predictive system for chronic disease diagnosis Forest, Support Vector Machines, or Deep Learning
using machine learning and deep learning. models like CNNs for image data.
Below is the step-by-step explanation:  Criteria: Accuracy, speed, and compatibility with dataset
type.
1. Dataset Collection
 Purpose: Gather data required for the model, which 4. Splitting Dataset
includes both textual data (symptoms, demographics)  Purpose: Divide the dataset into training and testing
and imaging data (X-rays, CT scans). subsets.
 Sources: Hospitals, medical records, publicly available  Split Ratio: Typically 80% for training and 20% for
datasets like UCI Machine Learning Repository or Kaggle. testing.
2. Dataset Preprocessing  Why: Training helps the model learn patterns, while
 Purpose: Clean and prepare the raw data for analysis. testing evaluates its performance on unseen data.
 Steps: Handle missing values (e.g., replacing or removing 5. Model Evaluation
them).Normalize numerical data (e.g., scaling age  Purpose: Assess the model's performance and
between 0 and 1).Encode categorical variables (e.g., effectiveness.
gender as binary).Perform image preprocessing (e.g.,
resizing, normalization).  Metrics: Accuracy Percentage of correct predictions.
 Precision and Recall: For disease-specific evaluations.
 Outcome: A consistent and clean dataset ready for input
into machine learning models.  F1-Score: Balances precision and recall.
3. Selecting ML Model 6. Output
 Purpose: Choose the most suitable machine learning or  Purpose: Provide meaningful results or predictions.
deep learning model for prediction.

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