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@article{180003,
        author = {Raghav Sant and Aman Kakran and Sachin Mahla and Uday Kuchhal},
        title = {Healthcare Application Using ML & AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {501-508},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180003},
        abstract = {— Healthcare has witnessed a transformative 
shift with the integration of Machine Learning (ML) 
and Artificial Intelligence (AI), enabling early disease 
detection, personalized medicine, and operational 
efficiency. This project focuses on developing MLbased predictive models for chronic diseases, 
specifically diabetes, heart disease, and kidney disease, 
which collectively pose significant global health 
challenges. By leveraging publicly available datasets, 
the project applies advanced data preprocessing 
techniques and employs multiple ML algorithms, 
including Random Forest, Logistic Regression, and 
Decision Trees, to achieve accurate disease predictions.
The methodology involves cleaning and preparing 
datasets, selecting critical features, and training 
models to identify patterns indicative of these diseases. 
Model evaluation is conducted using metrics such as 
accuracy, precision, recall, and F1-score, ensuring 
robust and reliable performance. Insights from feature 
importance analysis highlight key health indicators, 
aiding in clinical decision-making.
The project emphasizes real-world applicability by 
exploring the integration of predictive models into 
healthcare workflows, enabling early diagnosis and 
timely interventions. Additionally, it addresses 
challenges such as dataset bias, model interpretability, 
and ethical considerations related to data privacy and 
AI adoption in healthcare.
This research underscores the potential of AI and ML 
to enhance diagnostic accuracy, improve patient 
outcomes, and optimize clinical workflows. Future 
work involves expanding the models to include diverse 
datasets and incorporating real-time patient data for 
dynamic predictions. By harnessing the power of AI, 
this project demonstrates a scalable and impactful 
approach to addressing global healthcare challenges.},
        keywords = {Machine Learning (ML), Artificial  Intelligence (AI, style, Chronic Disease Prediction, Clinical Decision Support},
        month = {May},
        }