Klasifikasi Diabetes Menggunakan Model Pembelajaran Ensemble Blending

Abstract

Diabetes mellitus is one of the deadliest disease and it is increasing in occurrence through the world. This can be prevented by conducting early diagnosis and treatment. However, in developing countries, less than half of people with diabetes are diagnosed correctly which lead to lose of human lives. In this Big Data era, medical databases have enormous quantities of data about their patients. But this medical data may contain noise and a lot of useless information which may mislead the expert in making a decision for medical diagnosis. Data mining is a technique to that is very effective for medical applications for identifying patterns and extracting useful information for databases. This paper proposed a data mining approach using an ensemble blending method to tackle a diabetes prediction problem in Pima Indian Diabetes Dataset. We proposed a blending ensemble classifier approach using a combination of Decision Tree and Logistic Regression as base classifiers, and Support Vector Machine as a top blender classifier. Our approach reached accuracy of 81% and F1-score of 0.81 proves to be higher when compared with basic classifier without combination.
 Index Terms—diabetes, ensemble, data mining

Klasifikasi Penyakit Diabetes Menggunakan Algoritma Naive Bayes

“…Diabetes mellitus atau lebih sering disebut diabetes merupakan penyakit metabolis yang kronis yang mana penyakit diabetes terjadi karena kurangnya jumlah insulin yang cukup disebabkan ketidakmampuan pankreas dalam tubuh untuk memproduksi hormon insulin. Kekurangan insulin dapat mengakibatkan gula darah di dalm tubuh mengalami jumlah yang berlebihan, kondisi ini dapat menyebabkan komplikasi pada ginjal dan organ tubuh lainnya [1]. Insulin merupakan hormon yang disekresi oleh pankreas dalam tubuh manusia [2].…”

Comparison of Support Vector Machine Performance with Oversampling and Outlier Handling in Diabetic Disease Detection Classification

“…One of the machine learning methods that can be used to detect diabetes is a support vector machine [9]. SVM is a method that separates two classes using a linear function so that a dividing line (hyperplane) is found between classes [10]. The advantage of SVM is that it is very effective in completing classification, so it can quickly separate each positive and negative point [11].…”