XGBoost Classifier with Hyperband Optimization for Cancer Prediction Based on Geneselection by Using Machine Learning Techniques

Swathi, Kommana; Kodukula, Subrahmanyam

doi:10.18280/ria.360502

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Swathi Kommana, et al [113] conducted research focused on cancer prediction utilizing gene data. In their study, the researchers employed normalized data along with scaling features to process patient gene data.…”

Section: ) Data Normalizationmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: ) Data Normalizationmentioning

confidence: 99%

“…On cancer prediction, using the XGBoost algorithm combined with the feature selection technique resulted in a prediction accuracy [113]. The XGBoost algorithm was superior to the RF algorithm.…”

Section: A Ensemble Learningmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…While XGboost [19] has good results in all angles, there are still some problems, one of which is that it has many parameters, and several combinations of parameters get several result scores. In the field of algorithm parameter search, the genetic algorithm gives the excellent results and solves the optimal solution.…”

Section: Ga-xgboost Algorithmmentioning

confidence: 99%

Classification Predictive Maintenance Using XGboost with Genetic Algorithm

Khiat¹,

Hebri²,

Besma³

2022

RIA

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This study develops a condition classification system of compressor 103J and water pump systems which are key equipment in the ammonia production line, hence the monitoring of these two very important machines. In recent years, there are many good intelligent machine learning algorithms and XGboost is one of them. However, it contains many parameters and classification performance of the model will be greatly affected by the selection of parameters and their combination technique. In this paper, XGboost algorithm is combined with the genetic algorithm, called GA-XGboost, in order to find the best hyper parameters of classifiers which makes the classifier more efficient and ensures the proper functioning of compressor 103J and water pump systems. Experiments show that GA-XGboost algorithm has improved the accuracy of classification in the compressor 103J and the water pump dataset compared with other machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF) and AdaBoost. Also experiments demonstrate the improvement of the GA-XGboost algorithm by the combination of different selection and crossover operators of the genetic algorithm.

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“…MLR is a statistical method that models a linear relationship between the independent variable and the dependent variable, MLR is evaluated using the lowest RMSE and MAE and the highest R 2 [59]. PCA is a method for reducing data dimensions in images, and the main objective of the PCA method is to reduce dimensions so that image data is easier to process [60]. In this study PCA was used with MLR as a comparison algorithm called PCR.…”

Section: 𝑐𝑓 =mentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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XGBoost Classifier with Hyperband Optimization for Cancer Prediction Based on Geneselection by Using Machine Learning Techniques

Cited by 6 publications

References 19 publications

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Classification Predictive Maintenance Using XGboost with Genetic Algorithm

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

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