Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

Wirasati, Ilsya; Rustam, Zuherman; Aurelia, Jane Eva; Hartini, Sri; Saragih, Glori Stephani

doi:10.11591/ijai.v10.i2.pp430-437

Cited by 4 publications

(5 citation statements)

References 24 publications

(33 reference statements)

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“…Therefore, it is feasible that other researchers may make hypotheses utilizing this unique approach and make an effort to refute our findings in regions where they are more prevalent. Comparable studies, however, lacked a control group, and the bulk of studies [7,[46][47][48][49][50]59,63] similarly paid little attention to the group sizes we believed to be equal. As a consequence, we think that our study offers a more accurate evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…With numerous ranges of training data, the algorithm has accuracy, recall, and precision are 98.99%, 100%, and 98.20%, respectively. Thalassemia diagnostic involves data from 150 individuals from Indonesia's Hospital, with 10 attributes for SVM [47] base classification with a variety of kernel functions, i.e., polynomial, linear, and RBF. Gaussian RBF kernel gives 99.63% accuracy.…”

Section: Classifiers For Beta Thalassemiamentioning

confidence: 99%

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Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem,

Aslam,

Lali

et al. 2023

Diagnostics

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Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

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Section: Discussionmentioning

confidence: 99%

Section: Classifiers For Beta Thalassemiamentioning

confidence: 99%

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem,

Aslam,

Lali

et al. 2023

Diagnostics

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“…Each kernel carries its strengths and is selected based on the data's nature and the specific task at hand [146]. The linear kernel is adequate for linearly separable data, while the RBF and polynomial kernels are better suited for non-linear relationships [147]. This flexibility allows SVMs to deliver strong performance across various scenarios, from simple linear separations to complex, high-dimensional classification and regression tasks.…”

Section: Decision Treementioning

confidence: 99%

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Basu,

Saha,

Banerjee

et al. 2024

Energies

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This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

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“…TF is the frequency of occurrence of a word in a certain document, while IDF is the inverse value of the number of documents containing a certain term. The TF-IDF formula of a term is shown in equation ( 8) [26].…”

Section: 𝑤 = ∑ 𝛼 𝑖 𝑦 𝑖 𝑥 𝑖 𝑁 𝑖=1mentioning

confidence: 99%

Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

Husni,

Arif Muntasa,

Mochamad Dani Hartanto

2023

Technium

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The COVID-19 pandemic has resulted in significant changes in the education sector. The government issued a policy so that learning must be carried out online from home. This policy became a polemic for teachers and students so that pro and con opinions emerged on social media, especially Twitter. Sentiment analysis of public opinion is an interesting study. Standard classification algorithms such as k-Nearest neighbours, naïve bayes, decision tree, random forest, and support vector machine (SVM) can categorize these opinions in a short time with good accuracy. Many studies show that SVM is more accurate than all other classification methods. SVM works using kernels, including Linear, Polynomial and Radial Basis Functions (RBF) where each kernel requires different parameters. The linear kernel only requires one parameter, namely c (Cost). The RBF kernel requires 2 parameters, c and ɣ (gamma) while the Polynomial kernel uses 2 parameters, c and degrees. SVM does not have default values for these parameters and are based on experience and experimentation. The wider the range of parameters, the more likely the classifier obtains the optimal values. This study tries some parameters values of SVM kernels for text classification based on sentiment. Testing using 5-fold cross validation and confusion matrix show that SVM with a linear kernel provides the best performance with an accuracy of above 84%.

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Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

Cited by 4 publications

References 24 publications

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

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