Longitudinal Neuroimaging Data Classification for Early Detection of Alzheimer’s Disease using Ensemble Learning Models

Tripathi, Satvik; Augustin, Alisha Isabelle; Kim, Edward

doi:10.36227/techrxiv.19295120

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…We discuss how researchers have used Adaboost and Random Forest (RF) classification to classify species, traffic signs, cancer cells, crops, and many other things in the second section of related work. Random Forest is utilized in medical sector predictions, such as neuroimaging data in alzheimer's disease [12], optimal drug therapy [13], prediction of early kidney transplant [14] [15], COVID-19 prediction using patient's symptoms [16]. Ensemble method-based architecture using random forest predicts employee's turn over [17], for classifying urban land cover [18], in soil data [19][20], in twitter sentiment analysis with the polarity detection task in [21][22] [23].…”

Section: Related Workmentioning

confidence: 99%

A Modified Boosted Ensemble Classifier on Location Based Social Networking

Kumar

2023

JIOS

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One of the research issues that researchers are interested in is unbalanced dataclassification techniques. Boosting approaches like Wang's Boosting and ModifiedBoosted SVM (MBSVM) have been demonstrated to be more effective for unbalanceddata. Our proposal The Modified Boosted Random Forest (MBRF) classifier is aRandom Forest classifier that uses the Boosting approach. The main motivation of thestudy is to analyze sentiment of geotagged tweets understanding the state of mind ofpeople at FIFA and Olympics datasets. Tree based model Random Forest algorithmusing boosting approach classifies the tweets to build a recommendation system withan idea of providing commercial suggestions to participants, recommending localplaces to visit or perform activities. MBRF employs various strategies: i) a distancebasedweight-update method based on K-Medoids ii) a sign-based classifier eliminationtechnique. We have equally partitioned the datasets as 70% of data allocated fortraining and the remaining 30% data as test data. Our imbalanced data ratio measured3.1666 and 4.6 for FIFA and Olympics datasets. We looked at accuracy, precision,recall and ROC curves for each event. The average AUC achieved by MBRF on FIFAdataset is 0.96 and Olympics is 0.97. A comparison of MBRF and Decision tree modelusing 'Entropy' proved MBRF better.

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Section: Related Workmentioning

confidence: 99%

A Modified Boosted Ensemble Classifier on Location Based Social Networking

Kumar

2023

JIOS

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“…Artificial Intelligence has taken a huge leap in the field of medicine, fostering more advanced diagnostic and prognostic outcomes. We have seen AI algorithms working in a completely clinical setting to detection of Covid using binary features [1, 2, 3, 4, 5]. The future of the ‘Standard’ medical practice will be here sooner than anticipated, where a patient will be seeing a computer before seeing a doctor through advances in artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

RadGenNets: Deep Learning-Based Radiogenomics Model For Gene Mutation Prediction In Lung Cancer

Tripathi

Augustin

Moyer

et al. 2022

Preprint

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In this paper, we present our methodology that can use for predicting gene mutation in patients with non-small cell lung cancer (NSCLC). There are three major types of gene mutations that an NSCLC patient's gene structure can change epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and Anaplastic lymphoma kinase (ALK). We worked with the clinical and genomics data for each patient as well CT scans. We preprocessed all of the data and then built a novel pipeline to integrate both the image and tabular data. We built a novel pipeline that used a fusion of Convolutional Neural Networks and Dense Neural Networks. Also, using a search approach, we pick an ensemble of deep learning models to classify the separate gene mutations. These models include EfficientNets, SENet, and ResNeXt WSL, among others. Our model achieved a high area under curve (AUC) score of 94% in detecting gene mutation.

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“…In addition to diagnosis, AI can aid in the prediction of cancer patient survival rates, such as lung cancer patients. In the field of radiology, artificial intelligence (AI) is being utilized to diagnose disorders in patients using CT scans, MR imaging, and X-rays [4, 11, 12, 1, 13]. Alongside, the question of fairness and ethics has also become very crucial as more and more techniques are getting ready to be implemented in a clinical setting [14, 15, 16, 17].…”

Section: Introductionmentioning

confidence: 99%

Turing Test Inspired Method for Analysis of Biases Prevalent in Artificial Intelligence-Based Medical Imaging

Tripathi

Augustin

Dako

et al. 2022

Preprint

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Background: Because of the growing need to provide better global healthcare, computer-based and robotic healthcare equipment that depend on artificial intelligence have seen an increase in development. In order to evaluate artificial intelligence (AI) in computer technology, the Turing test was created. For evaluating the future generation of medical diagnostics and medical robots, it remains an essential qualitative instrument.Method: We propose a novel methodology to assess AI-based healthcare technology that provided verifiable diagnostic accuracy and statistical robustness. In order to run our test, we used a State-of-the-art AI model and compared it against radiologist for checking how generalized of the model is and if any biases are prevalent. Results: We achieved results that can evaluate the performance of our chosen model for this study in a clinical setting and we also applied a quantifiable methods for evaluating our modified turing test results using a meta-analytical evaluation framework. Conclusion: This test provides a translational standard for upcoming AI modalities. Our modified Turing Test is a notably strong standard to measure the actual performance of the AI model on a variety of edge cases and normal cases and also helps in detecting if the algorithm is biased towards any one type of case. This method extends the flexibility detect any prevalent biases and also classify the type of bias.

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Longitudinal Neuroimaging Data Classification for Early Detection of Alzheimer’s Disease using Ensemble Learning Models

Cited by 3 publications

References 11 publications

A Modified Boosted Ensemble Classifier on Location Based Social Networking

A Modified Boosted Ensemble Classifier on Location Based Social Networking

RadGenNets: Deep Learning-Based Radiogenomics Model For Gene Mutation Prediction In Lung Cancer

Turing Test Inspired Method for Analysis of Biases Prevalent in Artificial Intelligence-Based Medical Imaging

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