Healthcare Management System to Improve Data Analytics Using Support Vector Machine Algorithm

Knowledge discovery in databases (KDD) is concerned with progress in data analysis tools. Data mining is a crucial parts of the KDD process. Data mining is a method for discovering and extracting patterns from huge medical datasets. There are a number of benefits to using the Support Vector Machine (SVM) algorithm in healthcare systems. SVM is a strong and flexible machine learning algorithm that may excel at certain healthcare analytics applications. SVM is preferred in healthcare systems because of their adaptability, ability to manage imbalanced datasets, feature interpretability, handling of high-dimensional data, handling of classification, predictive modeling tasks, handling of non-linear relationships, resistance to overfitting and provision of a distinct margin of separation. It is essential to carefully consider the unique needs and features of the healthcare application when choosing an algorithm. Healthcare systems that use SVM algorithms do so with the intention of capitalizing on SVM's capabilities to tackle domain-specific problems and tasks. In light of this development, the relevant literature on this topic with regard to approach, SVM algorithm, and outcomes are surveyed. The publications evaluated in this study have been categorized according to their relevance to the disciplines, models, tasks, and approaches discussed. Data mining is the practice of sifting through massive amounts of unstructured data for meaningful patterns and insights. Results show 90% accuracy and 98% precision and F measures of the given medical dataset.