A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm

With the extremely rapid growth of data in various industries, big is gradually recognized and valued by people. Medical data, which can best reflect significance value, has also received attention from parties. In Saudi Arabia, healthcare quality assessment mostly based on human experience basic statistical methods. this paper, we proposed a model medical region integrated traditional evaluation methods machine learning techniques. Healthcare been accurate effective after noise processing, outliers could certain information. An improved k-nearest neighbors (KNN) algorithm its time complexity have reduced to be more suitable for processing. outlier indicator established KNN algorithm. Experimental results showed that approach good potential detecting hospitals with financial fraud poor-quality care.