• Varsha G
• M Maheswari
• A S Balaji

Machine learning (ML) has emerged as a transformative technology in the healthcare sector, enabling advanced data analysis and decision-making in areas such as disease prediction, diagnosis, treatment optimization, and personalized medicine. This paper presents a comparative study of various ML algorithms used in healthcare, analyzing their strengths, weaknesses, and suitability for different healthcare applications. The study covers a range of algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, naive Bayes, neural networks, k-means clustering, and reinforcement learning. Each algorithm is evaluated in the context of specific healthcare tasks such as disease prediction, medical image analysis, patient classification, and treatment recommendation. The paper also highlights the trade-offs between model accuracy, interpretability, computational requirements, and data dependencies, which are crucial considerations when deploying ML models in clinical environments. By providing insights into the applicability and limitations of these algorithms, this study aims to guide healthcare professionals and data scientists in selecting the most appropriate machine learning models for various healthcare challenges, ultimately improving patient outcomes and healthcare efficiency.

Machine learning (ML), Healthcare sector, Disease prediction, ML algorithms, Patient outcomes

"A Comparative Study of Machine Learning Algorithm for Healthcare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d774-d778, March-2025, Available :http://www.jetir.org/papers/JETIR2503386.pdf

"A Comparative Study of Machine Learning Algorithm for Healthcare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd774-d778, March-2025, Available at : http://www.jetir.org/papers/JETIR2503386.pdf