Logistic regression ensemble to classify Alzheimer gene expression

Kuswanto, Heri; Werdhana, Reynaldi Wisnu

doi:10.1109/icon-sonics.2017.8267818

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Various classification methods with the main goal of achieving high accuracy and least misclassification are found in literature. 16 proposed the use of logistic regression ensemble (LORENS) in the process of classification where the genes are classified into just two groups and has an efficiency of 75% for a partition with a minimum of five partitions. 16,17 One of the most efficient tools for classification of AD is the support vector machines (SVM), which generates a hyperplane that acts as a criterion in the separation of disease-causing and non-disease-causing genes.…”

Section: Related Workmentioning

confidence: 99%

“…16 proposed the use of logistic regression ensemble (LORENS) in the process of classification where the genes are classified into just two groups and has an efficiency of 75% for a partition with a minimum of five partitions. 16,17 One of the most efficient tools for classification of AD is the support vector machines (SVM), which generates a hyperplane that acts as a criterion in the separation of disease-causing and non-disease-causing genes. 3,[18][19][20] However, as the sample size becomes extremely higher, the efficiency gradually decreases.…”

Section: Related Workmentioning

confidence: 99%

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Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

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Alzheimer’s disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

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“…The machine learning approach to building predictive models is a new breakthrough. Several machine learning approaches that will be used are Logistic Regression (LR) and Support Vector Machine (SVM) [12,13].…”

Section: Introductionmentioning

confidence: 99%

Detection of landslide events using logistic regression and support vector machine

Suroso,

Syahrul Ramadhan

2023

E3S Web Conf.

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This research explores newspaper records regarding earthquake events during the Dutch colonial period in Padang, West Sumatra. This note is vital amidst the difficulties in acquiring historical statistical data for studying disasters in the past. The method used in this research is the historical method by integrating information in newspapers, particularly those that discuss the situation of society and the policies of the Dutch colonial government in dealing with various earthquakes taking place in the city of Padang during the colonial period. This research is expected to open new insights for developing the discipline of disaster science and provide access to the development of historical disaster studies in disaster-prone countries, in this case, in Indonesia.

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“…36:1 (2020) 43-49 | www.matematika.utm.my | eISSN 0127-9602 | LORENS has been applied to some cases, such as the prediction of consumer defection [7], the classification of gene expression for Alzheimer's disease [8], and the classification analysis of enzymes [9]. The data has been used for analysis using some machine learning methods, such as random forest, SVM [10,11], K-Nearest Neighbor (KNN), and Extreme Gradient Bossting (XGB) Kimura.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Ensemble (LORENS) Applied to Drug Discovery

2020

Self Cite

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Logistic regression is one of the commonly used classification methods. It has some advantages, specifically related to hypothesis testing and its objective function. However, it also has some disadvantages in the case of high-dimensional data, such as multicolinearity, over-fitting, and a high computational burden. Ensemblebased classification methods have been proposed to overcome these problems. The logistic regression ensemble (LORENS) method is expected to improve the classification performance of basic logistic regression. In this paper, we apply it to the case of drug discovery with the objective of obtaining candidate compounds to protect the normal non-cancerous cells, which is considered to be a problem with a data-set of high dimensionality. The experimental results show that it performs well, with an accuracy of 69% and AUC of 0.7306.

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Logistic regression ensemble to classify Alzheimer gene expression

Cited by 3 publications

References 9 publications

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Detection of landslide events using logistic regression and support vector machine

Logistic Regression Ensemble (LORENS) Applied to Drug Discovery

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