General Framework for Biomedical Knowledge With Data Mining Techniques

Data mining is the process which automates the extraction of predictive information discovers the interesting knowledge from large amounts of data stored in information repositories. Biomedical informatics (BMI) is the science underlying acquisition, maintenance, retrieval, collecting, manipulating, and analysing the biomedical knowledge and information to improve medical data analysis, problem solving, and decision making, inspired by efforts toward progress in medical domain. In this research work a comprehensive framework will be generated which comprises of various data mining techniques and evaluate meaningful information from biomedical data. Data mining field will be applied to biomedical data to analyze the characteristics, identify patterns of interest, for diagnosing and predicting patients' health. These proposed biomedical data mining framework useful to the scholars who are interested in the related researches of data mining and medical domain. Data mining is a replacement for another popularly used term "Knowledge Discovery in Databases". This technique widely used in many domains including education, finance, commerce, human resource, geological surveys, weather pattern prediction and telecommunications. KDD process brings together extract knowledge from data in the context of large databases which interest to researchers in machine learning, biomedicine, pattern recognition, statistical analysis, artificial intelligence, knowledge acquisition and data visualization. There are various phases involved in mining data as following. Data Integration: The data are collected and integrated from all the different sources. Data Selection: It may not all the data has collected in integration. To select relevant data used for data mining. Data Cleaning: The data have collected are not clean and may encompass errors, inconsistent, missing values, duplicate, noisy or irrelevant data. Data Transformation: The data even after cleaning are not ready for mining as need to transform them into unique forms for mining. Data mining methods used to attain this smoothing, aggregation, normalization. Data Mining: The data to discover the interesting patterns and techniques like clustering and association analysis. Pattern Evaluation and Knowledge Presentation: It includes visualization, conception, transformation, renovation removing redundant patterns from the produced patterns. Discovered Knowledge: It helps to make use of the knowledge acquired to take better decisions. Biomedical informatics derives knowledge