AN INNOVATIVE APPROCH FOR DIABETIC PREDICTION

COMPUTER ENGINEERING

Diabetes is one of the most deadly and persistent diseases that can result in a spike in blood glucose levels. The primary goal of this study is to examine a database of diabetic patients in order to predict diabetic disease at an early stage. The Naive Bayes Classification is utilized to predict diabetes in this suggested experiment system. Information mining is the process of extracting data from a dataset and transforming it into a usable structure for further use. The diabetic patients' informative collection was built by gathering data from the clinic storeroom, which has 1865 occurrences of various qualities. The results suggest that the proposed innovative strategy can predict diabetes more precisely (0.96) than traditional/existing techniques. The output of this suggested system employing the Naive Bayes Classifier will be a Web Interface that displays the outcome of having diabetes or not having diabetes based on input variables such as insulin level, age, and so on. This improves the system's accuracy. The Naive Bayes method is a supervised learning technique for addressing classification issues that are based on the Bayes theorem. It is mostly utilized in text classification tasks that require a large training dataset. The Naive Bayes Classifier is a simple and effective classification method that aids in the development of fast machine learning models capable of making quick predictions. It's a probabilistic classifier, which means it makes predictions based on an object's probability. Spam filtration, sentiment analysis, and article classification are all common uses of the Naive Bayes Algorithm.

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