Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naïve Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics' diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.
Undoubtedly, blindness is a major trauma, which affects an individual not only physically but also emotionally. There are approximately 46 million visually impaired people throughout the world. It is becoming a global problem. In India alone, 19 million people are totally blind or else have visual defects. Out of this 19 million, 15 million reside in rural areas. India is among the countries which suffers from a shortage of doctors. There are only about 12,000 ophthalmologists in India, with most concentrating their practice in urban localities. Additionally, the inadequate infrastructures of roads, telecommunication, transport and financial status of the patients make it even more difficult to provide health care in rural areas. Teleophthalmology is a new branch of telemedicine that offers solutions to this serious problem. This paper discusses Indian teleophthalmology projects known as Sankara Netralaya Teleophthalmology Project (SNTOP) and Aravind Teleophthalmology Network (ATN). These have proven successful in the state of Tamilnadu, India, both in rural and secondary healthcare centers.
The objective of our paper is to predict the chances of diabetic patient getting heart disease. In this study, we are applying Naïve Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Diabetes is a set of related diseases in which body cannot regulate the amount of sugar specifically glucose (hyperglycemia) in the blood. The diagnosis of diseases is a vital role in medical field. Using diabetic"s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.
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