In the current scenario, diabetes is considered as a widely spread disease globally. This issue is a matter of great concern and the disease is spreading at an alarming rate across the country. We can analyse, visualize the data appropriately and forecast the chances of having diabetes for a person, with the highest level of accuracy and exactness. This indefatigable investigation and papers aim to analyze, compare different neural networks, machine learning algorithms and classifiers which can predict the probability of disease in patients. the results obtained from the proposed methods are assessed using recollection techniques and making assessments based on exactness of the outputs, which are tested for a number of cases consisting of correct forecasts and wrong forecasts. A thorough study is done on diabetes dataset and experiments have been carried out using Neural Networks and several different classifiers.
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