Abstract
AbstractHealthcare industry is advancing at a lightning speed with extensive usage of IT tools and techniques. The use of machine learning algorithms is not only restricted to the field of computer science. It has sneaked into the healthcare industry too and is assisting the medical practitioners in the prediction of the onset of several diseases based on a particular set of attributes like age, BMI, blood pressure, glucose and insulin level and so on. Diabetes is one such disease that is growing at a very rapid rate and is pretty fatal leading to the requirement of a promising prediction system to diagnose the onset of the disease before it silently attacks the patients and causes the avoidable damage to health. Machine Learning techniques are doable in mining the diabetes dataset to efficiently classify and predict the disease. In this study, four machine learning classification algorithms are compared to find the more viable one in classifying a diabetic and a non-diabetic. KeywordsDiabetesClassificationPIDD (Pima Indian Diabetes Dataset)LDA (Linear Discriminant Analysis)kNN (k-Nearest Neighbour)RF (Random Forest)SVM (Support Vector Machine)ANN (Artificial Neural Network)
Published Version
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