AN INTRODUCTION AND REVIEW ON MACHINE LEARNING APPLICATIONS IN MEDICINE AND HEALTHCARE

• Published in: 2019 IEEE Conference on Information and Communication Technology pp. 1 - 6
• Main Authors: Singh, Preeti, Singh, S. P., Singh, D. S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Decision Tree; Decision trees; Diseases; Forestry; Machine learning; Medical diagnostic imaging; Medicine; Random Forest; Support Vector Machine; Support vector machines; Task analysis
• DOI: 10.1109/CICT48419.2019.9066250

Machine learning techniques can extensively apply in the solution of the medicine domain problems by applying classification models and systems that can support medical personnel in the diagnosis and predication of diagnosis diseases. Though, it's hard to extract knowledge and information from medical records and data because this data and information is in mixed, unorganized, and high dimensional. This data also contains noise in collected data and outliers exist in collected data. Main applicable method will be used applies by checking different machine learning techniques. The performance of machine learning technique is checked by verifying and validating machine learning techniques' performances through accuracy. Present research paper has been discussing about the usability and applicability of different machine learning techniques i.e. decision tree algorithm, support vector machine method, random forest method, evolutionary algorithms based models and swarm intelligence based techniques in the diagnosis and treatment of the diseases. Advance medical diagnosis criteria generates confidence in diagnosis by using imagining techniques in the diagnosis of a disease is extensively used by doctors. In view of the fact that analyzing medical images is very complex and difficult task, by using machine learning methods for analysis of imaging will support and give major help in disease diagnosis. Application of different Machine learning methods is used by applying its techniques on big data for interpretation for diagnosis because machine learning methods show their capability and shows their easiness to solve the problems of bioinformatics domain.