A Review on Random Forest: An Ensemble Classifier

Parmar, Aakash; Katariya, Rakesh; Patel, Vatsal

doi:10.1007/978-3-030-03146-6_86

Cited by 241 publications

(114 citation statements)

References 9 publications

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“…In combination with ML approaches, we predicted neurotoxin-induced perturbations in hMOs. RF is by design a useful technique for reducing predictive variability, preventing overfitting and achieving high classification accuracy [12]. Importantly, RF gives estimates of which variables are most important in the classification [10].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Monzel

Hemmer

Kaoma

et al. 2020

Parkinsonism & Related Disorders

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Brain organoids are highly complex multi-cellular tissue proxies, which have recently risen as novel tools to study neurodegenerative diseases such as Parkinson's disease (PD). However, with increasing complexity of the system, usage of quantitative tools becomes challenging. Objectives: The primary objective of this study was to develop a neurotoxin-induced PD organoid model and to assess the neurotoxic effect on dopaminergic neurons using microscopy-based phenotyping in a high-content fashion. Methods: We describe a pipeline for a machine learning-based analytical method, allowing for detailed imagebased cell profiling and toxicity prediction in brain organoids treated with the neurotoxic compound 6-hydroxydopamine (6-OHDA). Results: We quantified features such as dopaminergic neuron count and neuronal complexity and built a machine learning classifier with the data to optimize data processing strategies and to discriminate between different treatment conditions. We validated the approach with high content imaging data from PD patient derived midbrain organoids. Conclusions: The here described model is a valuable tool for advanced in vitro PD modeling and to test putative neurotoxic compounds.

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Section: Discussionmentioning

confidence: 99%

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Monzel

Hemmer

Kaoma

et al. 2020

Parkinsonism & Related Disorders

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“…In combination with a powerful machine learning approach for the analysis of multivariate profiling data, we were able to predict neurotoxin-induced perturbations in the human midbrain organoid system. Random forest by design is a well-established technique for reducing predictive variability, preventing overfitting and achieving high classification accuracy (Parmar et al, 2019). Importantly, random forest gives estimates of which variables are most important in the classification (Breiman, 2001).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Monzel

Hemmer

Mukendi

et al. 2019

Preprint

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AbstractA major challenge in the field of neurodegenerative diseases is the poor translation of pre-clinical models to clinical applications. The human brain is an immensely complex structure, which makes it difficult to recapitulate its development, function and disorders. In the recent years, brain organoids derived from human induced pluripotent stem cells have risen as novel tools to study neurodegenerative diseases such as Parkinson’s disease (PD). PD is a multifactorial disorder, with aging, genetics and environmental factors as key etiological elements. The majority of the PD cases are idiopathic and proposed to result from a complex interaction between genetic predisposition and environmental exposure. Consequently, the identification of potentially disease causing environmental factors is of critical importance. Organoids, as complex multi-cellular tissue proxies, are an ideal tool to study cellular response to environmental changes. However, with increasing complexity of the system, usage of quantitative tools becomes challenging. This led us to develop an automated high-content image analysis pipeline for image-based cell profiling in the organoid system. Here, we introduce a midbrain organoid system that recapitulates features of neurotoxin-induced PD, representing a platform for machine-learning-assisted prediction of neurotoxicity in high-content imaging data. This model is a valuable tool for advanced in vitro PD modeling and for the screening of putative neurotoxic compounds.

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“…A random forest algorithm was chosen for classification because of its higher performance with imbalanced data when compared to other machine learning classifiers [31][32][33][34]. A random forest algorithm is an ensemble classifier that combines a specified number of decision trees and takes the majority decision to predict classification, thus preventing overfitting.…”

Section: Classificationmentioning

confidence: 99%

Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm

Dasariraju¹,

Huo

McCalla³

2020

Bioengineering

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Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders the function of blood cells and the immune system. The current AML diagnostic method, a manual examination of the peripheral blood smear, is time consuming, labor intensive, and suffers from considerable inter-observer variation. Herein, a machine learning model to detect and classify immature leukocytes for efficient diagnosis of AML is presented. Images of leukocytes in AML patients and healthy controls were obtained from a publicly available dataset in The Cancer Imaging Archive. Image format conversion, multi-Otsu thresholding, and morphological operations were used for segmentation of the nucleus and cytoplasm. From each image, 16 features were extracted, two of which are new nucleus color features proposed in this study. A random forest algorithm was trained for the detection and classification of immature leukocytes. The model achieved 92.99% accuracy for detection and 93.45% accuracy for classification of immature leukocytes into four types. Precision values for each class were above 65%, which is an improvement on the current state of art. Based on Gini importance, the nucleus to cytoplasm area ratio was a discriminative feature for both detection and classification, while the two proposed features were shown to be significant for classification. The proposed model can be used as a support tool for the diagnosis of AML, and the features calculated to be most important serve as a baseline for future research.

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A Review on Random Forest: An Ensemble Classifier

Cited by 241 publications

References 9 publications

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm

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