scholarly journals CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM

SINERGI ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 177
Author(s):  
Ardina Ariani ◽  
Samsuryadi Samsuryadi
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Kidney Diseases ◽  
K Nearest Neighbor ◽  
Abc Algorithm ◽  
Bee Colony

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.

scholarly journals Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square

2021 ◽  
Vol 7 (2) ◽  
pp. 11-22
Author(s):  
Ghinaa Zain Nabiilah ◽  
Said Al Faraby ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Feature Selection ◽  
Everyday Life ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Way Of Life ◽  
Chi Square ◽  
The Law ◽  
Test Scenarios

Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.

Artificial Bee Colony Algorithm for Traveling Salesman Problem

2011 ◽  
Vol 314-316 ◽  
pp. 2191-2196 ◽  
Author(s):  
Wei Hua Li ◽  
Wei Jia Li ◽  
Yuan Yang ◽  
Hai Qiang Liao ◽  
Ji Long Li ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Traveling Salesman ◽  
Comparison Study ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
Bee Colony Optimization

By combining the modified nearest neighbor approach and the improved inver-over operation, an Artificial Bee Colony (ABC) Algorithm for Traveling Salesman Problem (TSP) is proposed in this paper. The heuristic approach was tested in some benchmark instances selected from TSPLIB. In addition, a comparison study between the proposed algorithm and the Bee Colony Optimization (BCO) model is presented. Experimental results show that the presented algorithm outperforms the BCO method and can efficiently tackle the small and medium scale TSP instances.

scholarly journals Product Review Based Customer Sentiment Analysis using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Feature Selection Problem

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

Chronic Kidney Disease Prediction Using Data Mining Algorithms

2021 ◽  
pp. 92-111
Author(s):  
Devesh Kumar Srivastava ◽  
Pradeep Kumar Tiwari
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Big Data Analytics ◽  
K Nearest Neighbor ◽  
Contemporary World ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Logistic Regression Algorithm

In today's contemporary world, it is important to know about the odds of having a disease because of changing living standards of the population overall in the continent. The disease on which the authors are working is chronic kidney disease. Once the person gets chronic kidney disease (CKD), his working capability decreases along with other adverse effects. It is possible to get rid of diseases like CKD with new methodologies that will help us to predict the stage of kidney disease at an early stage. Under big data analytics, data may be structured, unstructured, quasi- or semi-structured. The CKD detected and predicted by applying classification models: support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression algorithm. It helps in predicting the likelihood of occurrence of disease on various different features. The two algorithms KNN and SVM are compared to find the algorithm that gives better accuracy. Further regression technique has been used to detect the disease based on, which the stages are classified by using GFR (glomerular filtration rate) formula.

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