Automated Differential Diagnosis in Medical Systems Using Neural Networks, kNN and SOM

• Main Authors: Isola, R., Carvalho, R., Iyer, M., Tripathy, A. K.
• Format: Conference Proceeding
• Language: English
• Subjects: Data mining; Decision support system; Diabetes; Diseases; Hospitals; KNN classification; Medical diagnostic imaging; Neural Network; SOM; Vectors
• DOI: 10.1109/DeSE.2011.20

The amount of Medical data recorded in hospitals and its significance as an ever-growing source of information has been long known and proven. Though the importance of the information hidden in these records has never been doubted, this data has mostly been used only for clinical purposes. Only recently has this been properly mined for valuable information to be used for research and to develop systems that assist the medical fraternity. Mostly, the systems that make use of this information are domain specific systems that predict diseases restricted to their area of specialization (like heart, brain etc.). But these systems are limited and are not applicable to the whole medical dataset. Our system uses this vast storage of information so that diagnosis based on this historical data can be made. This system aids medical diagnosis in the whole dataset by computing the probability of occurrence of a particular ailment from the medical data. The system mines the data using a unique algorithm which increases accuracy of such diagnosis by combining Neural Networks and Differential Diagnosis all integrated into one single approach. The strengths of kNN, Hop field algorithm, SOM and P2P Grid Architecture are used to make the system unique and effectively enhanced.