Revolutionizing Patient Care With Data-Driven Healthcare Applications: A “Machine Learning” And Predictive Analytics Framework

Abstract

Introduction: The main principle of this research is to identify the importance of “Machine learning” and predictive analysis for the development of the healthcare sector.

 Literature Review: With this method, treatment plans may be changed in real-time, guaranteeing that the patient's needs will always be met. Artificial Intelligence (AI) has a subset called “Machine learning” (ML). Equipped with 'Training Data,' or historical data, the machine algorithm makes use of statistical models and algorithms

Methodology: For this study, “primary quantitative data collection” was used to gather data. It is making certain that the results are founded on firsthand, first-hand accounts from the participants. Seventy individuals were chosen as a sample to reflect a wide range of pupils with impairments.

Findings: "IBM SPSS software" was used to analyze the data that was gathered. The researchers were able to do in-depth data analysis thanks to SPSS, a potent statistical analysis program. These include using SPSS for “hypothesis testing, correlation analysis, and descriptive statistics”

Discussion and Conclusion: As per this research, it has been concluded that digital tools greatly increase access to health and other data, providing healthcare practitioners with a comprehensive perspective of patient health. With this data, they may create therapies that are specific to each patient's requirements, save healthcare expenses, and prevent sickness.