Data mining of medical datasets with missing attributes from different sources essay
In practice, many datasets from the medical domain are incomplete and contain some incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem: Data mining has become a popular knowledge discovery tool, showing good results in the fields of marketing, social sciences, finance and medicine 19, 20. Recently, there have been multiple classification algorithms applied to medical datasets to perform predictive analysis on patients and their medical diagnosis, 6, 9, 10, 21 1. Introduction. The healthcare industry generates large amounts of complex data every day from multiple data sources, such as electronic health records, medical reports, hospital equipment and billing systems Strang and Sun, 2020. These massive amounts of data generated by healthcare transactions are too complex and voluminous The author used PIMA's Indian Women Dataset who was concerned with the health characteristics of women. For this dataset, different models were trained under different hyperparameters. The author suggested creating advanced models on RF due to its highest accuracy and ability to avoid overfitting. Different models were compared. Communication between sensors distributed throughout healthcare systems may cause some missing features in the transferred functions. Fixing the data problems of sensing devices through artificial intelligence technologies has enabled the Medical Internet of Things MIoT and its emerging applications in healthcare. MIoT has great potential to use the resulting model in contrast. real medical data. Fig. Matrices are used for visual comparison of the real and synthetic datasets in the case of cardiovascular data. Therefore, it is the job of the Data Scientist or Machine Learning Engineer to decide whether their algorithm can work if the missing data remains unchanged. The method to keep the missing data untouched is defined below. We define the function for handling missing data, which takes the source DataFrame as an argument and returns it. The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal that publishes articles on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, humanities and arts. The scope of the journal includes descriptions of data. Many real-world medical datasets contain some missing attribute values. In general, missing value imputation can be performed to solve this problem, namely providing estimates for the missing values through a reasoning process based on the entire observed data. However, if the observed data does contain some, in this article I will briefly explain and list some methods that can be used to deal with missing data, using some practical examples. Basic Steps Using central tendencies to attribute values. Average Median Mode 2 The column with the missing data is deleted. 3 Fill the column with new values. Example