Diabetic disease classifier based on three machine learning models / ‘Ayuni Zamri ... [et al.] - Zamri, ‘Ayuni; Darmawan, Mohd Faaizie; Mohamed Hatim, Shahirah; Zainal Abidin, Ahmad Firdaus; Osman, Mohd Zamri (Universiti Teknologi MARA, Perak, 2022)
Diabetes is generally acknowledged as an increasing epidemic that affects nearly every country, age group, and economy on the earth. Without doubt, this worrisome statistic requires immediate response. The healthcare business produces vast volumes of complicated data on regular basis from a variety of sources, including electronic patient records, medical reports, hospital gadgets and billing systems. Traditional approaches cannot handle and interpret the massive volumes of data created by healthcare transactions because they are too complicated and numerous. Machine learning has been used to many sectors of medical health due to the rapid growth of the technology. The aim of this research is to aid the medical professionals to diagnose patients whether the patients is diabetic or not diabetic, by applying machine learning algorithms, and evaluate the results to find the best algorithm to predict diabetic diseases. Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Random Forest (RF) are implemented in this research. Performance measures which is accuracy score is utilized to determine the performance for each model. Based on the results for each model, the model with the highest accuracy score obtained is SVC Linear with the score of 78.62%. The proposed models are valuable to be used for medical practice or in assisting medical professionals in making treatment decisions.