The book would provide an in-depth guide to the various techniques and methods used in preprocessing and preparing medical datasets for classification tasks. The book would start by discussing the importance of data preprocessing in machine learning and how it affects the overall performance of a classifier.
It would then cover various topics such as data cleaning, data transformation, normalization, outlier detection, and imputation, with a focus on their applications in medical datasets. The book would also delve into feature scaling, selection, and encoding categorical variables, providing readers with practical examples and case studies.
Additionally, the book would explore the challenges posed by class imbalance and multi-collinearity in medical datasets, and provide techniques for data balancing and data reduction. The book would also provide guidance on feature engineering and its impact on the performance of classifiers.
The book would be aimed at data scientists, machine learning engineers, and medical professionals with a background in data analysis and programming who are interested in using machine learning to classify medical datasets. The book would provide a comprehensive and hands-on approach to preprocessing medical datasets for classification tasks, equipping readers with the knowledge and skills necessary to tackle real-world problems.
Data mining engine can perform functions like Characterization, Association and Correlation Analysis, Classification, Prediction, Cluster analysis, Sequential patterns, Outlier analysis, and Evolution analysis. Besides the
effectiveness of data mining, there are also many challenges faced while performing data
mining task. The factors influencing data mining are: Mining Methodology and User
Interaction, Performance Issues, Diverse Data Types, Uncertainty Handling, Dealing with Missing
Values and Outliers, Efficiency of Algorithms, Incorporating Domain Knowledge, Size
and Complexity of Data.
