Random forest and support vector machine based hybrid liver disease detection

Assegie, Tsehay Admassu; Rajkumar, Subhashni; Kumar, N. Komal; Manivannan, Jijendira Prasath; Duraisamy, Pradeep; Engidaye, Minychil Fentahun

doi:10.11591/eei.v11i3.3787

Cited by 11 publications

(4 citation statements)

References 18 publications

(19 reference statements)

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“…Further, we have evaluated different ML models, and found RF's superior performance compared to DT, SVM, KNN, MLP and LR. RF has been widely utilised in numerous dataset and showed well performances in terms of diabetes patient detection 33 , heart disease detection 34,35 , liver disease detection 36 and Parkinson's disease detection 37 . A combination of LR and RF models gave the highest score for the prognosis of type-II diabetes.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for detecting DNA attachment on SPR biosensor

Mondal

Ahmed

Birbilis

et al. 2023

Sci Rep

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Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

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Section: Discussionmentioning

confidence: 99%

Machine learning for detecting DNA attachment on SPR biosensor

Mondal

Ahmed

Birbilis

et al. 2023

Sci Rep

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“…Four machine learning algorithms namely generalized linear model (GLM) [22], logistic regression (LR) [23], DT [24], and random forest (RF) [25] have been selected for comparison in this study. These five algorithms were selected based on the preliminary findings from the AutoModel module in the RapidMiner software that uses optimization search strategy to identify the suitable algorithms for the given dataset.…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics

Masrom,

Abdul Samad,

Septiyanti

et al. 2024

Bulletin EEI

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Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.

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“…The random forest [26] creates decision trees on data mockups and gains the prediction from all the formed decision trees. Finally, it elects the best elucidation by voting means.…”

Section: E Random Forestmentioning

confidence: 99%

Liver Disease Prediction and Classification using Machine Learning Techniques

Tokala¹,

Hajarathaiah²,

Gunda³

et al. 2023

IJACSA

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Recently liver diseases are becoming most lethal disorder in a number of countries. The count of patients with liver disorder has been going up because of alcohol intake, breathing of harmful gases, and consumption of food which is spoiled and drugs. Liver patient data sets are being studied for the purpose of developing classification models to predict liver disorder. This data set was used to implement prediction and classification algorithms which in turn reduces the workload on doctors. In this work, we proposed apply machine learning algorithms to check the entire patient's liver disorder. Chronic liver disorder is defined as a liver disorder that lasts for at least six months. As a result, we will use the percentage of patients who contract the disease as both positive and negative information We are processing Liver disease percentages with classifiers, and the results are displayed as a confusion matrix. We proposed several classification schemes that can effectively improve classification performance when a training data set is available. Then, using a machine learning classifier, good and bad values are classified. Thus, the outputs of the proposed classification model show accuracy in predicting the result.

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Random forest and support vector machine based hybrid liver disease detection

Cited by 11 publications

References 18 publications

Machine learning for detecting DNA attachment on SPR biosensor

Machine learning for detecting DNA attachment on SPR biosensor

Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics

Liver Disease Prediction and Classification using Machine Learning Techniques

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