Knowledge Discovery in Health Domain using Deep Neural Network Algorithms

Knowledge discovery in databases (KDD) primarily depends on finding a strategy for effectively processing data. Data mining is a critical phase in the KDD process for extracting a valuable pattern from a dataset. Pattern extraction and discovery are complex processes that often require a vast dataset. Several applications and systems are used for health care and clinical data to diagnose and record patient records. The installed systems' primary goal is to extract a relevant pattern capable of improving healthcare services. To improve healthcare services, data mining necessitates the right design and execution of data mining algorithms to detect a unique pattern from large amounts of data. As a result, we propose using patient information from the Hewa Hospital in Sulamani, which is in charge of cancer and blood diseases, as a case study for our research. The primary goal of this research is to look at deep neural networks (DNN) and artificial neural networks (ANN) as classification algorithms that can assist us in making better judgments. The results show that the DNN algorithm outperforms the ANN method. When utilizing a 70:30 training and testing dataset, the score can reach 87.84.