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Vol. 8, Issue 2 (2019)

Improved machine learning-driven patient health prediction system

Author(s):
Vikas Singhal
Abstract:
In the healthcare industry, the ability to accurately forecast a patient's health status is critical. With the growing availability of patient data and the advancement of machine learning techniques, there is a growing interest in using machine learning algorithms to predict patient outcomes. We propose an improved machine learning-driven patient sickness or health status prediction system in this project. The ability to predict a patient's health status properly is essential in the medical. The approach of machine learning algorithms to forecast patient outcomes is becoming more popular as a result of the expansion of patient data availability and the development of machine learning methodologies. In this study, we provide an enhanced machine learning-driven method for predicting patient illness or health condition. The results show that our system beats existing methods in terms of accuracy and efficiency. The proposed system is tested on a sizable dataset of patient records. The system is made to be scalable and adaptable to various healthcare settings, making it an important tool for healthcare providers to use in patient outcome prediction. In conclusion, our suggested machine learning-driven system for predicting a patient's illness or health state is a potential strategy for enhancing patient outcomes in the healthcare sector. We can give medical personnel insightful information about patient health by utilizing machine learning and data analytics, empowering them to make wiser decisions and deliver more efficient care.
Pages: 870-875  |  80 Views  35 Downloads


The Pharma Innovation Journal
How to cite this article:
Vikas Singhal. Improved machine learning-driven patient health prediction system. Pharma Innovation 2019;8(2):870-875. DOI: 10.22271/tpi.2019.v8.i2n.25421

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