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@article{173555,
        author = {Kashish Aggarwal and Khushi Prakash and Devanshi Bhagat and Keshav Gupta and Dr. Sudhir Dawra},
        title = {Health Guard:Multiple Disease Prediction System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1266-1273},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173555},
        abstract = {Diseases such as diabetes, heart disease, and cancer impact millions of people around the world, Highlighting the importance of accurate and early diagnosis. Machine Learning (ML) presents a groundbreaking method for automating disease prediction, improving accuracy, and reducing human errors in healthcare decision-making. This study examines the application of three key ML algorithms—Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression—to predict diseases using various datasets. Each algorithm is assessed for its predictive performance, efficiency, and appropriateness for different scenarios.
Random Forest is recognized for its strength and capability to manage large datasets, effectively minimizing overfitting and achieving high accuracy. SVM is particularly useful for intricate, high-dimensional data but may demand more computational power. Logistic Regression, although more straightforward, offers valuable insights into the relationships between variables, making it ideal for binary classification tasks.
The paper features comprehensive comparisons and visual representations, emphasizing the advantages and drawbacks of each method. The findings suggest that the integration of ML techniques can transform healthcare by facilitating quicker, more dependable diagnoses, ultimately enhancing patient outcomes and optimizing resource allocation. This study highlights the promise of ML in predictive healthcare and its potential to influence the future of disease management and prevention.},
        keywords = {Machine Learning (ML), Disease Prediction, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, Healthcare, Predictive Analytics, Early Diagnosis, Healthcare Automation, Disease Management, Data Analysis, Binary Classification, High-Dimensional Data, Overfitting, Computational Efficiency, Patient Outcomes, Resource Optimization, Predictive Healthcare.},
        month = {March},
        }