Diabetes menjadi salah satu penyakit yang mematikan di dunia, termasuk Indonesia. dapat menyebabkan komplikasi banyak bagian tubuh dan secara keseluruhan meningkatkan risiko kematian. Salah cara untuk mendeteksi diabetes adalah dengan memanfaatkan algoritma machine learning. Logistic regression merupakan model klasifikasi dalam learning digunakan analisis klinis. Pada makalah ini, dirancang pre...

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis required as soon possible. Machine learning technique has become reliable for medical treatment. With help a machine classifier algorithms, doctor can detect disease on time. For this perspective, prediction been discussed in article. dataset taken from UCI repository. Seven algorithms have applied research...

Diabetes mellitus is a disease that attacks chronic metabolism, characterized by the body’s inability to process carbohydrates, fats so glucose levels are high. sixth cause of death in world. Classifying data about diabetes makes it easier predict disease. As technology develops, can be detected using machine learning methods. The method done support vector machine. advantage SVM very effective...

Supporting sampling in the presence of joins is an important problem in data analysis. Pushing down the sampling operator through both sides of the join is inherently challenging due to data skew and correlation issues between output tuples. Joining simple random samples of base relations typically leads to results that are non-random. Current solutions to this problem perform biased sampling o...

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in diffe...

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we...

MOTIVATION With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional ty...

Many real-world domains present the problem of imbalanced data sets, where examples of one classes significantly outnumber examples of other classes. This makes learning difficult, as learning algorithms based on optimizing accuracy over all training examples will tend to classify all examples as belonging to the majority class. We introduce a method to deal with this problem by means of creati...

This paper deals with inducing classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining classes (majority classes). The minority class is usually of primary interest and it is required to recognize its members as accurately as possible. Class imbalance constitutes a difficulty for most algorithms learning classifiers as they are bi...

Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of sources significantly impact production costs. Accurate load forecasting for specific region would allow more efficient management, planning, scheduling low-cost generation units ensuring on-time delivery full mo...