Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.
Diabetes mellitus generally referred to as diabetes is reaching epidemic proportions in India and all around the world. The degree of disease and destruction due to diabetes and complications connected with diabetes is enormous, and originated a significant health care burden on both households and society. Deficiency of knowledge about diabetes causes untimely death among the population at large. Thus, developing a technique that should spread awareness about diabetes may affect the people. In this book, a mobile/android application based solution to overcome the lack of awareness about diabetes has been presented. The application uses machine learning techniques to predict risk of readmission to the hospital in diabetics. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes risk of readmission prediction using UCI dataset. Due to the lack of knowledge many people even don’t know that they have diabetes, this will lead to a serious problem, as duration of unknown disease increases the risks associated with it also increases. Hospital readmission is a high-priority health care quality measure and target for cost reduction. Reducing readmission rates of diabetic patients have the potential to greatly reduce health care costs while simultaneously improving care. In this book, a novel mobile application based solution for this problem is provided. This mobile app, MobDBRCal (Risk of Readmission in Diabetes) will act as an important tool that can help in predicting the chances of risk of readmission in diabetes and also provides knowledge about this chronic disease.
Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).
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