THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS
نویسندگان
چکیده
ABSTRAK- Sickness is an unusual condition of the body or mind that causes discomfort, malfunction, suffering to sick person. One disorder occurs due a lack health concerns thyroid disease. The butterfly-shaped endocrine gland near neck's bottom. diagnosis disease complicated because symptoms can fluctuate based on rise and fall hormones, which increase utilization oxygen by body's cells. In this case, examination doctor proper interpretation clinical data required identify However, limitations age time constraints lead patient data. Therefore, study was conducted analysis classification simplify speed up process diagnosing using Xgboost Multiclass method, expected get accuracy value above 90%. Keywords: Classification, Thyroid, Multiclass, Machine Learning
منابع مشابه
Using Two-Class Classifiers for Multiclass Classification
The generalization from two-class classification to multiclass classification is not straightforward for discriminants which are not based on density estimation. Simple combining methods use voting, but this has the drawback of inconsequent labelings and ties. More advanced methods map the discriminant outputs to approximate posterior probability estimates and combine these, while other methods...
متن کاملInteractive multiclass segmentation using superpixel classification
This paper adresses the problem of interactive multiclass segmentation. We propose a fast and efficient new interactive segmentation method called Superpixel Classification-based Interactive Segmentation (SCIS). From a few strokes drawn by a human user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SCIS uses superpixel over...
متن کاملAccelerating the XGBoost algorithm using GPU computing
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An ...
متن کاملMulticlass Classification Calibration Functions
In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for easily converting bounds for the surrogate risk (which can be computed through well-known methods) into bounds for the true risk, the probability of making a mistake. They are particularly suitable in non-parametric sett...
متن کاملHierarchical Multiclass Object Classification
Humans can use similarity between objects in order to recognize rare objects. They also make many abstract concepts when they see some objects very often. Interestingly, a large part of brain is associated with common classes like faces rather than rare objects like Ostrich. In our work we want to propose a model that has four mentioned characteristics. 1. Use more resources for categories that...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Jusikom : Jurnal Sistem Informasi Ilmu Komputer
سال: 2022
ISSN: ['2580-2879']
DOI: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2831