Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data
Uzair Aslam Bhatti, Yuan Linwang, Zhaoyuan Yu, Saqib Ali Nawaz, Anum Mehmood, Mughair Aslam Bhatti, Qurat ul Ain Zeeshan, Mir Muhammad Nizamani, Zeeshan, Shengjun Xiao
Abstract
Healthcare diseases are spreading all around the globe day to day. Hospital datasets are full from the data with much information. It's an urgent requirement to use that data perfectly and efficiently. We propose a novel algorithm for predictive model for eye diseases using KNN with machine learning algorithms and artificial intelligence (AI). The aims are to evaluate the connection between the accumulated preoperative risk variables and different eye diseases and to manufacture a model that can anticipate the results on an individual level, thus giving relevance to impactful factors and geographic and demographic features. Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to health through the available resources. With the increasing demand for medical services and the limited allocation of medical resources, the application of health economics in clinical practice has been paid more and more attention, and it has gradually played an important role in clinical decision-making.