An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

Arowolo, Micheal Olaolu; Adebiyi, Marion O.; Nnodim, Chiebuka T.; Abdulsalam, Sulaiman Olaniyi; Adebiyi, Ayodele Ariyo

doi:10.48048/wjst.2021.9849

Cited by 9 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous ML approaches have been proposed in the literature to enhance gene expression data classification such as clustering, classification, dimensional reduction, among others 23 . Training of ML models using initial high-dimensional features performs unsatisfactorily in practice and may result in network over-fitting and increased redundant information.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Machine learning approaches for classification of Plasmodium falciparum life cycle stages using single-cell transcriptomes

Ghosh

Shukla

Choudhuri

2022

Preprint

View full text Add to dashboard Cite

Malaria spread by female anopheles mosquito is a highly fatal disease widespread in many parts of the world, causing 0.4 million deaths globally. Vital gene expressions form the basis in the detection of malaria infection levels. Quantification of malaria parasite infected RBCs and classification of its life cycle stages are done at macroscopic level by experts, for making informed decisions. Off late multiple computational approaches have been proposed to circumvent the problem of dimensionality leading to accurate predicted results. In this work a dimensionality reduction technique based on Genetic Algorithm (GA) is applied on P. falciparum single-cell transcriptomics to arrive at an optimized subset of features from the larger dataset. Features are chosen based on their class variants considering increased efficiency and accuracy, to separately transform the selected elements into a lower dimension. For the classification of the life cycle of malaria parasite based on single cell transcriptome data, a three-pronged approach employing the multiclass Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) techniques is used. Further, we constructed protein interaction networks of the genes identified by the feature selection method and elucidate the role of the proteins in progression of the parasite through it's life cycle. Our approach presents a novel protocol to implement ML techniques on scRNA seq datasets and subsequently harnessing the extracted information for biomarker/drug target detection.

show abstract

Section: Discussion and Outlookmentioning

confidence: 99%

Machine learning approaches for classification of Plasmodium falciparum life cycle stages using single-cell transcriptomes

Ghosh

Shukla

Choudhuri

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A classification accuracy of 92.0% is achieved with MFCC-based SVM while only 44.0% is achieved with CRP-based SVM. SVM is also found to be an effective classification technique for analysing RNA-seq datasets from the mosquito Anopheles gambiae to predict Malaria Vector Gene Expression where up to 98% of accuracy score is achieved (9) .…”

Section: Related Workmentioning

confidence: 99%

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Bora¹,

Barman²,

Patowary³

et al. 2023

IJST

View full text Add to dashboard Cite

Objectives: A melody is made up of several musical notes or pitches that are joined together to form one whole. This experiment aims to develop four models based on the Mel-frequency Cepstral Coefficients (MFCC) to classify the melodies played on harmonium corresponding to five different class of Assamese folk Music. Methods: The melodies of five different categories of Assamese folk songs are selected for classification. With the help of expert musicians, these melodies are played in harmonium and audio samples are recorded in the same acoustic environment. 20 MFCC's are extracted from each of the samples and classification of the melodies is done using four supervised learning techniques-Decision Tree Classifier, Linear Discriminant Analysis (LDA), Random Forest Classifier, and Support Vector Machine (SVM). Findings: The performance of the fitted models are evaluated using different evaluation techniques and presented. A maximum of 94.17% average accuracy score is achieved under Support Vector Machine. The average accuracy scores of Decision Tree Classifier, Linear Discriminant Analysis (LDA), and Random Forest Classifier are 73.58%, 85.58%, and 86.11% respectively. The models are developed based on 250 samples (50 from each type). However, increasing the training sample size, there is a possibility to improve the performances of the other three models also. Novelty: The developed approach for identifying the melodies is based on computational techniques. This work will certainly provide a basis for conducting further computational studies in folk music for any community.

show abstract

“…Data preprocessing techniques include eliminating redundant values and feature extraction using chi-square. The research [16] developed an adaptive genetic algorithm model for malaria data classification. The recursive features elimination (RFE) model is used to select relevant features in the data.…”

Section: Introductionmentioning

confidence: 99%

Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy

Khoirunnisa,

Ramadhan

2023

J.INFOTEL

View full text Add to dashboard Cite

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.

show abstract

An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

Cited by 9 publications

References 29 publications

Machine learning approaches for classification of Plasmodium falciparum life cycle stages using single-cell transcriptomes

Machine learning approaches for classification of Plasmodium falciparum life cycle stages using single-cell transcriptomes

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy

Contact Info

Product

Resources

About