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Informative Article | Science and Technology | India | Volume 10 Issue 7, July 2021
Data Preprocessing in Healthcare: A Vital Step towards Informed Decision-Making
Abstract: Healthcare data is becoming increasingly complex and voluminous, posing challenges in extracting valuable insights and enhancing healthcare services [1]. This paper emphasizes the pivotal role of data analysis in overcoming these challenges. The authors introduce the concept of healthcare data analysis and underscore its significance in improving patient outcomes, reducing healthcare costs, and enhancing care quality. Furthermore, the authors explore various healthcare data types, including electronic health records (EHRs), claims data, medical imaging data, and patient-generated data. They elucidate the techniques involved in data preprocessing, encompassing data cleaning, transformation, and integration. The paper also delves into exploratory data analysis (EDA), elucidating techniques such as data visualization, summary statistics, and correlation analysis to identify patterns and trends within healthcare data. In addition, the authors elucidate predictive modeling techniques in healthcare data analysis, including regression analysis, decision trees, and neural networks, all crucial for predicting patient outcomes and identifying risk factors. Moreover, the authors discuss the development of clinical decision support systems through data analysis, facilitating informed decision-making among healthcare professionals. Real-world examples are provided to illustrate the utility of data analysis in healthcare, such as predicting hospital readmissions, identifying high-risk patients, and enhancing medication adherence. Lastly, the paper explores emerging trends in data analysis within healthcare, particularly the integration of artificial intelligence and machine learning, and their potential to revolutionize the industry. In sum, this paper underscores the significance of data analysis in healthcare and its potential to bring about transformative changes.
Keywords: Data Analysis, Healthcare Data, Electronic Health Records (EHR), Claims Data, Medical Imaging Data, Data Preprocessing, Data Cleaning, Data Transformation, Data Integration, Exploratory Data Analysis (EDA), Data Visualization, Clinical Decision Support, Artificial Intelligence (AI), Machine Learning (ML)
Edition: Volume 10 Issue 7, July 2021,
Pages: 1523 - 1528