A Two-Step Approach for Classification in Alzheimer’s Disease

Falco, Ivanoe De; Pietro, Giuseppe De; Sannino, Giovanna

doi:10.3390/s22113966

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…In addition, OAB must be based on the symptom of urgency and should not be confused with other storage symptoms induced by medical disease [ 83 ]. Therefore, machine learning algorithms have potential for differentiating true OAB [ 84 ], analyzing brain activity for central sensitization [ 85 ], and detecting MetS early [ 86 ].…”

Section: Discussionmentioning

confidence: 99%

Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies

Hsu

Chen

et al. 2022

Biomedicines

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Metabolic syndrome (MetS) is defined by a group of cardiovascular risk factors, including impaired glucose tolerance, central obesity, hypertension, and dyslipidemia. Overactive bladder (OAB) syndrome consists of symptoms such as urinary urgency, frequency, and nocturia with or without urge incontinence. The high prevalences of metabolic syndrome (MetS) and overactive bladder (OAB) worldwide affect quality of life and cause profound negative impacts on the social economy. Accumulated evidence suggests that MetS might contribute to the underlying mechanisms for developing OAB, and MetS-associated OAB could be a subtype of OAB. However, how could these two syndromes interact with each other? Based on results of animal studies and observations in epidemiological studies, we summarized the common pathophysiologies existing between MetS and OAB, including autonomic and peripheral neuropathies, chronic ischemia, proinflammatory status, dysregulation of nutrient-sensing pathways (e.g., insulin resistance at the bladder mucosa and excessive succinate intake), and the probable role of dysbiosis. Since the MetS-associated OAB is a subtype of OAB with distinctive pathophysiologies, the regular and non-specific medications, such as antimuscarinics, beta-3 agonist, and botulinum toxin injection, might lead to unsatisfying results. Understanding the pathophysiologies of MetS-associated OAB might benefit future studies exploring novel biomarkers for diagnosis and therapeutic targets on both MetS and OAB.

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Section: Discussionmentioning

confidence: 99%

Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies

Hsu

Chen

et al. 2022

Biomedicines

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“…Alzheimer's disease [35][36][37][38][39][40] Autism Spectrum Disorders [41,42] Brain Tumors [43][44][45] Breast Diabetes [73][74][75][76][77][78] Exposure to extremely low frequency waves [79,80] Glioblastoma [81,82] Heart Failure [83][84][85][86][87] Kidney Disease [88,89] Lung Cancer [90][91][92][93][94] Melanoma [95,96] Multiple Sclerosis [97][98][99] Parkinson's Disease [100][101][102][103][104][105][106][107] Prostate Cancer [108,109] Rectal Cancer [110]…”

Section: Topic Referencementioning

confidence: 99%

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Bottrighi,

Pennisi

2023

Information

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Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

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“…The results outperform those of traditional supervised learning models. In [13] the authors used MRI images to check the presence of AD and to assess the degree of AD. An evolutionary algorithm is applied to extract specific information for AD classification.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

El-Gawady¹,

Tawfik²,

Makhlouf³

2023

Computers, Materials &Amp; Continua

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Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer's disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS phase, a hybrid filtering method and embedded method are used. In the classification phase, three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase. The final phase is to validate the performance of these classifiers using different metrics. The crux of this article is to select the most informative genes from the hybrid method, and the best ML technique to predict AD using this minimal set of genes. Five different datasets are used to achieve our goal. We predict AD with impressive values for MultiLayer Perceptron (MLP) classifier which has the best performance metrics in four datasets, and the Support Vector Machine (SVM) achieves the highest performance values in only one dataset. We assessed the classifiers using seven metrics; and received impressive results, allowing for a credible performance rating. The metrics values we obtain in our study lie in the range [.97, .99] for the accuracy (Acc), [.97, .99] for F1score, [.94, .98] for kappa index, [.97, .99] for area under curve (AUC), [.95, 1] for precision, [.98, .99] for sensitivity (recall), and [.98, 1] for specificity. With these results, the proposed approach outperforms recent interesting results. With these results, the proposed approach outperforms recent interesting results.

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A Two-Step Approach for Classification in Alzheimer’s Disease

Cited by 7 publications

References 58 publications

Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies

Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

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