An Analysis On Comparitive Study Of Diabetic Prediction Using Machine Learning
Nowadays, diabetes has become a typical disease to the mankind from young to the old persons. The expansion of the diabetic patients is increasing day-by-day because of various causes like toxic or chemical contents mix with the food, obesity, bad diet, change in lifestyles, eating habit etc. Therefore, diagnosing the diabetes is extremely essential to save human lives. The information analytics can be defined as a process of examining and identifying the hidden patterns from great amount of information to draw conclusions. In health care, this analytical process is carried out using machine learning algorithms which analyzes medical data in order to use the machine learning models for medical diagnoses.
In this paper, we have evaluated comparative study of different machine learning classifications algorithms for predicting diabetes more accurately. In this research work, we are analyzing the accuracy of different algorithms like: Linear Discriminant Analysis, Random Forest Classifier, Decision Tree Classifier, MLP Classifier in order to identify best classifier.
Keywords- Diabetes Prediction, Machine Learning Techniques: Data pre-processing, Linear Discriminant Analysis, Random Forest Classifier, Decision Tree Classifier, MLP Classifier.
Cite this Article
Abhinav Sengar,   "An Analysis On Comparitive Study Of Diabetic Prediction Using Machine Learning"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.8, Issue 1, pp.434-437, January 2020, Available at :http://www.ijedr.org/papers/IJEDR2001082.pdf