Healthcare It Solutions For Clinical Decision Support

Abstract

This research aims to utilize relevant characteristics to develop a machine learning-based prediction algorithm and propose a diagnostic system for diabetes that leverages medical data and various machine learning algorithms to enhance the accuracy of diabetes diagnoses and predictions. The study outlines three proposed methodologies, with a common pre-processing step that involves eliminating null values, converting data types, normalizing data, conducting exploratory data analysis, among other tasks. The pre-processed data is then utilized across the three methodologies. The dataset for this research was collected from multiple hospitals in South Kashmir through a direct questionnaire administered to individuals who have recently been diagnosed with diabetes or those who do not yet have diabetes but display some or all symptoms associated with the condition. This dataset encompasses various attributes of individuals, including age, family history, and depression. The first proposed methodology focuses on improving classification accuracy and early detection of diabetes, distinguishing between diabetic and non-diabetic individuals. It presents a diabetes prediction algorithm based on machine learning techniques that address several limitations of conventional classifiers and establish a robust connection between clinical conditions and blood glucose levels.