Study of machine learning algorithms for special disease prediction using principal of component analysis
The worldwide study on causes of death due to heart disease/syndrome has been observed that it is the major cause of death. If recent trends are allowed to continue, 23.6 million people will die from heart disease in coming 2030. The healthcare industry collects large amounts of heart disease data which unfortunately are not "mined" to discover hidden information for effective decision making. In this paper, study of PCA has been done which finds the minimum number of attributes required to enhance the precision of various supervised machine learning algorithms. The purpose of this research is to study supervised machine learning algorithms to predict heart disease. Data mining has number of important techniques like categorization, preprocessing. Diabetic is a life threatening disease which prevent in several urbanized as well as emergent countries like India. The data categorization is diabetic patients datasets which is developed by collecting data from hospital repository consists of 1865 instances with dissimilar attributes. The examples in the dataset are two categories of blood tests, urine tests. In this research paper we discuss a variety of algorithm approaches of data mining that have been utilized for diabetic disease prediction. Data mining is a well known practice used by health organizations for classification of diseases such as diabetes and cancer in bioinformatics research.
2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)