- Mayuri Vaish

# Using Naïve Bayes to Predict Diabetes

**Introduction: **Diabetes affects approximately 1.25 million American adults and children__ [1]__. Type II Diabetes, caused by a rise in insulin resistance__[2]__, resulting in hyperglycemia (high blood-sugar), yielding symptoms such as excessive thirst, frequent urination, fatigue, dizziness, headache and nausea__[3]__. The risk of type II diabetes has been highly correlated with obesity__[4]__, and it has been hypothesized that specific markers such as age__[5]__, and even gender__[6]__. Naïve Bayes has been successfully applied in medical diagnoses before, with high accuracy rates__[7]__, but not for diabetes. With these developments, it seemed possible that a mathematical classification model such as Naïve Bayes could be used to 'predict' diabetes based on probability outcomes.

research aims to achieve just that.

**Aim: **To determine whether the Naïve Bayes mathematical model serves as a suitable predictor for diabetes, given specific patient characteristics.

**Method: **The Naïve Bayes classifier was applied on a patient *given* three specific attributes of age, gender, and frame, based upon an existing Statcrunch__[8]__ diabetes database. A code was then developed (see __bit.ly/NaiveBayes__) and applied using (1) 10, randomly selected training data and (2) 20 training data.

**Results: **Using only three of the 16 given attributes of patients, initial trials with 10 training data should a mild yield of 40%. However, upon increasing it to 20, the accuracy of the model's predictions increased by 80%.

**Conclusion: **If only three highly-simplified categories could yield an 80% accuracy rate using only 20 training data, the Naïve Bayes model shows immense promise for future development. Although by no means can this machine-learning algorithm replace physicians and tests, it can serve as a guide for them, and possibly uncover previously unnoticed trends. Above all, these results show the power of mathematical computation across all realms, including healthcare.