• POOJA VISHWAKARMA
• Mr. Rajneesh Pachouri
• Mr. Anurag Jain

One of the most significant issues facing society today is healthcare for humans. In order to provide patients with the necessary care, it looks for the most accurate, reliable, and efficient disease diagnosis as soon as feasible. Since this detection is frequently a challenging endeavour, the medical industry needs assistance from other fields, such computer science and statistics. These fields are challenged to investigate novel approaches that go beyond the conventional ones. An extensive overview that steers clear of very specific features is required due to the multitude of evolving techniques. In order to do this, we suggest conducting a thorough analysis of machine learning as it relates to medical diagnostics. This review focuses on contemporary methods for developing machine learning that are used in the medical industry to diagnose human disorders in order to find intriguing patterns, generate meaningful predictions, and aid in decision-making. This work can thus assist researchers in learning about and, if needed, evaluating the application of machine learning techniques in their respective fields of expertise. One of the main causes of death in the world is heart disease, however mortality rates can be lowered with early detection. Prominent research has demonstrated that the most recent artificial intelligence (AI) can be utilised to assess a person's risk for heart disease. However, in order to obtain the most performance out of these AI models in the event that the number of users increases, earlier research did not take dynamic scalability into consideration. Our solution to this issue is Health FaaS, an AI-powered smart healthcare framework that uses a serverless computing environment and the Internet of Things (IoT) to reduce heart disease-related mortality and prevent financial losses by minimising misdiagnoses.

human disease; machine learning, artificial intelligence; big data, smart healthcare

" AI-Based Multi Disease Detection Using Random Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d823-d834, May-2024, Available :http://www.jetir.org/papers/JETIR2405394.pdf

" AI-Based Multi Disease Detection Using Random Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd823-d834, May-2024, Available at : http://www.jetir.org/papers/JETIR2405394.pdf