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@article{185598,
        author = {Ayush Gupta},
        title = {Advancements in Predictive Analytics Using Machine Learning: Techniques and Applications in Healthcare},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2165-2169},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185598},
        abstract = {Predictive analytics through Machine Learning (ML) has revolutionized healthcare systems by enabling data-driven decisions, early diagnosis, and personalized treatment recommendations. This research explores the application of ML algorithms to healthcare datasets for disease prediction and risk assessment. Using structured clinical data from benchmark datasets such as the UCI Heart Disease and PIMA Diabetes datasets, this study compares multiple supervised algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Networks (ANN). The focus is on identifying the most accurate, efficient, and generalizable models for patient risk classification. The research introduces a hybrid ensemble approach that integrates multiple classifiers to enhance prediction robustness. Evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are employed to quantify model performance. Experimental results demonstrate that ensemble-based models outperform individual classifiers in diagnostic accuracy, with Random Forest and XGBoost achieving an accuracy exceeding 90%. The outcomes highlight how predictive analytics can improve early disease detection and reduce healthcare costs. The study concludes that ML-based predictive analytics can serve as a vital component of clinical decision support systems, paving the way for precision medicine and improved patient outcomes.},
        keywords = {Machine Learning (ML), Predictive Analytics, Healthcare Data, Disease Prediction.},
        month = {October},
        }