Comparative analysis on classifier algorithms implemented for the prediction of diseases.

Machine learning has been a revolutionary method that enables machines to learn from various datasets and make decisions and predictions according to its evaluation. This method has been extensively used in medical field for the prediction of various diseases using different kind of datasets available. It has also been vastly used in the medical field to develop systems to analyse various graphs and diagnose diseases. While there exist various algorithms to implement machine learning, the efficiency and complexities vary from one algorithm to another. This project aims to conduct a comparative study on some of the important algorithms and their working on datasets to predict the occurrences of non-communicable diseases in an area. This project helps in finding the best algorithm in terms of their complexities in prediction of diseases that helps in the development of an efficient prediction system which further facilitates in taking preventive measures. A novel classifier algorithm will also be developed and would be tested and compared against the existing algorithms to calculate its efficiency. [ABSTRACT FROM AUTHOR]