Data Mining in Healthcare: Applying Data Mining and Machine Learning Techniques to Analyze Large Healthcare Datasets, Such as Electronic Health Records, For Improved Diagnosis, Treatment, and Patient Outcomes

Abstract

Background: The provided text discusses the application of data mining in healthcare, specifically in analyzing large healthcare datasets, such as electronic health records (EHRs), for improved diagnosis, treatment, and patient outcomes. 

Objectives: To apply data mining and machine learning techniques to analyze large healthcare datasets, such as electronic health records, for improved diagnosis, treatment, and patient outcomes.

Methods: Division of data into training and testing subsets and the use of evaluation metrics to assess model performance was done. It also discussed the software and tools commonly used in implementing data mining and analysis processes, such as Python or R-programming languages and relevant libraries and frameworks.

Results: The study examined several factors related to patients' demographics, health indicators, and customer satisfaction. The analysis of age and gender revealed that the average age for males was 61.70 years (SD = 13.40), while for females, it was 58.32 years (SD = 15.07). The comparison of cholesterol levels showed that males had an average level of 233.87 (SD = 78.90), whereas females had an average level of 210.01 (SD = 45.50), with a non-significant p-value of 0.2388. However, in terms of blood sugar levels, males had an average level of 170.82 (SD = 35.90) and females had an average level of 156.09 (SD = 22.20), with a significant p-value of 0.0335.

Conclusion: The study leveraged data mining techniques on EHR data to uncover valuable insights into patient health characteristics and customer satisfaction. It highlighted the potential for improved diagnosis, treatment, and patient outcomes by integrating data mining and analysis into healthcare practices.