Laureen is a Software Engineer for over 10 years, a Data warehouse Analyst/Engineer for 4 years. She has a bachelor’s degree on Information Technology from JKUAT. She has worked in the Finance for 3 years and Health space for over 9 years.
Project Summary
Project Title: Early Detection and Prediction of Diabetes in Patients
Abstract: Diabetes Mellitus is a chronic non-communicable illness (NCD), that happens when the pancreas produces not enough insulin, or when it over produces the insulin that the body is not able efficiently use. There are more than 8,700 diabetes related deaths recorded in Kenya in 2015. It is estimated that diabetes causes 3% of deaths in Kenya. The prevalence may rise to 4.5% (approximately over 380 million people worldwide) by the year 2025. Most diabetes patients cannot access healthcare services; therefore, the conditions are not treated for long which leads to either deaths or permanent disability, either through blindness, kidney disease or amputation. So early diagnosis of diabetes is vital, in helping with on time treatment, thereby decreasing deaths caused by the disease and help medical practitioners in better decision making. The study’s main agenda was to identify the key factors to be used in predicting diabetes, design a prediction model using ML (machine learning) algorithms, and select the suitable model to give the best results. The result shows the Random forest algorithm had the highest accuracy of 75.2%, held best for the analysis of diabetic data.