Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia

Bansal, Deepika; Chhikara, Rita; Khanna, Kavita; Gupta, Poonam

doi:10.1016/j.procs.2018.05.102

Cited by 101 publications

(42 citation statements)

References 24 publications

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“…Detection of dementia (including Alzheimer's disease) [20][21][22] Detection of anxiety [23][24][25] Medicalchain-Blockchain and healthcare [26][27][28] Genetics and genomics [29][30][31] Virtual reality in healthcare [32][33][34] Social media in healthcare [35][36][37] Robotic surgery [38][39][40]…”

Section: Emergent Topics Recommended Readingsmentioning

confidence: 99%

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

2019

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The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity. A lot of artificial intelligence techniques have been demonstrated to be effective in smart city applications like energy, transportation, retail and control. In recent decade, retardation of the adoption of data analytics algorithms and systems in healthcare has been decreasing, and there is tremendous growth in data analytics research on healthcare data. The results of analytics aim at improving people's quality of life as well as relieving the issue of medical shortages. In this special issue "Data Analytics in Smart Healthcare", thirteen (13) papers have been published as the representative examples of recent developments. Guest Editors also highlight some emergent topics and opening challenges in healthcare analytics which follow the visions of the movement of healthcare analytics research.

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Section: Emergent Topics Recommended Readingsmentioning

confidence: 99%

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

2019

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“…A. Alzheimer's early signs and symptoms [20]  [25]. In this paper we use OASIS Dataset for studying the Alzheimer's dieses.…”

Section: Alzheimer's Diesesmentioning

confidence: 99%

“…Boise et al acknowledge prior research of low levels of clinical evaluation and diagnosis and conclude a possible explanation for this in the originality of the symptoms of dementia associated with the constraints encountered by physicians in their clinical practice. Through modern neuropsychological and cognitive testing, Alzheimer's disease is clinically diagnosed and other evidence of intellectual deficiency is verified [25]. According to Dubois et al, in addition to the clinical measures, the recommendations for the diagnosis of Alzheimer's disease accentuate the role that distinct biomarkers can perform.…”

Section: Introductionmentioning

confidence: 99%

Exploring of Classification Methods for Early Detection of Alzheimer’s Disease

2019

ijeat

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Alzheimer's disease (AD) is a degenerative brain disease, a common health problem in elderly pesople which causes decline in memory and affected on nerve cells. AD has different stages like mild congestive impairment (MIC) (early stage), moderate (middle stage), severe (late stage) it is essential to detect AD early in MIC, so that pre-emptive measures can be taken. Significant research was carried out over the past century to diagnose and detect this disease early. The objective of the article is provide a review evaluation and critical analysis of the recent research work done to early diagnosis of AD using Machine Learning Strategies.

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“…Conducting vocalization analyses is timeconsuming and often subject to software malfunction, both of which may contribute to the paucity of studies. 30,31 Thus, it is important to discern the potential utility of automated methods of data analysis in studies of pup vocalization production. [24][25][26][27] For other forms of behavioral analysis, large efforts have been made to automate data collection, such as video tracking in Caenorhabditis elegans models 28 or gesture tracking in mice.…”

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confidence: 99%

“…29 This effort is not limited to preclinical models, as recent studies from the field of neurology have focused on automated algorithms for diagnosing dementia and detecting biomarkers of Alzheimer's in clinical samples. 30,31 Thus, it is important to discern the potential utility of automated methods of data analysis in studies of pup vocalization production. In summary, the purpose of the present study was: (a) to extend previous findings to heterozygous females, (b) to extend previous findings to another strain by characterizing vocalization behavior in female WT and Fmr1 HETs in the FVB.129 strain and (c) to compare the detection rate of a high-throughput automated analysis method to previously described methods of hand-cleaning in the present model.…”

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confidence: 99%

High‐throughput analysis of vocalizations reveals sex‐specific changes in Fmr1 mutant pups

Nolan

Hodges

Lugo

2019

Genes Brain and Behavior

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There have been several reports that individuals with Fragile X syndrome (FXS) and animal models of FXS have communication deficits. The present study utilized two different call classification taxonomies to examine the sex-specificity of ultrasonic vocalization (USV) production on postnatal day (PD8) in the FVB strain of Fmr1 knockout (KO) mice. One classification protocol requires the investigator to score each call by hand, while the other protocol uses an automated algorithm. Results using the hand-scoring protocol indicated that male Fmr1 KO mice exhibited longer calls (P = .03) than wild types on PD8. Male KOs also produced fewer complex, composite, downward, short and two-syllable call-types, as well as more frequency steps and chevron call-types. Female heterozygotes exhibited no significant changes in acoustic or temporal aspects of calls, yet showed significant changes in call-type production proportions across two different classification taxonomies (P < .001). They

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Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia

Cited by 101 publications

References 24 publications

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

Exploring of Classification Methods for Early Detection of Alzheimer’s Disease

High‐throughput analysis of vocalizations reveals sex‐specific changes in Fmr1 mutant pups

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