Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern

Kim, JeeYoung; Lee, Min Kyoung; Wang, Sheng‐Min; Kim, Nak-Young; Kang, Dong Woo; Um, Yoo Hyun; Na, Hae-Ran; Woo, Young Sup; Lee, Chang Uk; Bahk, Won‐Myong; Kim, Donghyeon; Lim, Hyun Kook

doi:10.30773/pi.2020.0304

Cited by 23 publications

(12 citation statements)

References 43 publications

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“…Besides, it showed advantages in antinoise performance [26]. RF could process continuous and discrete data simultaneously [27] and possess significant advantages in processing high-dimension data, such as simplicity, realizability, and low computing cost [28]. The results of the research revealed that the prediction accuracy of RF training set samples reached 98.33%.…”

Section: Discussionmentioning

confidence: 97%

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Shang

Zhang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer’s disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer’s disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group ( P < 0.01 ). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group ( P < 0.001 ). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score ( R 2 = 0.1702 , 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001 ). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model ( P < 0.01 ). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model ( P < 0.01 ). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.

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Section: Discussionmentioning

confidence: 97%

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Shang

Zhang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…GC-MS technology was used to determine the changes of volatile components in tobacco before and after mildew. Some studies have shown that substances with large changes are obtained by comparing before and after mildew, and then the content changes of these substances are used to establish a model, so as to infer whether it is mildew [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Jiang

Zhang

Huang

et al. 2022

Scientific Programming

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In order to further improve the identification efficiency of tobacco mildew, a rapid identification model of tobacco mildew based on random forest algorithm was proposed in this study. In order to ensure the feasibility and pertinence of the model study, this study takes redried leaf tobacco as the research object, selects high-temperature and high-humidity environment as the experimental conditions, and obtains the sample data of the degree of tobacco mildew under different experimental conditions. At the same time, this paper constructs a rapid identification model of tobacco mildew with the help of random forest algorithm. Through the model experimental results, it is found that the accuracy of the model for the rapid identification of training samples can reach 93.82%, while the accuracy of independent testing is 94.84%. The experimental results fully reflect the availability and efficiency of the random forest algorithm model in the rapid identification of tobacco mildew.

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“…Teaching and research ability [23] includes the ability to carry out various special lectures; the ability to participate in the first and second classroom teaching work; and the ability to carry out theoretical research and project application in combination with work.…”

Section: Decision Tree Algorithm the Decision Tree Algorithmmentioning

confidence: 99%

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

Sun

et al. 2022

Wireless Communications and Mobile Computing

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The good development of industry-specific universities (hereinafter referred to as characteristic universities) promotes the optimal allocation of national university resources and moreover promotes the development of China’s education, while counselors, as important teaching and research staff, have a very close connection with the development level of the school, directly reflecting the quality and level of education of the school and determining the height of its educational development. Talent is the first productive forces, counselors as teaching talents, and is essential to the construction of industry specialty universities. The evaluation index system of teaching ability of counselors can effectively guide counselors toward the path of specialization, scientifically realize career planning, and maximize the achievement of the general goal of the characteristic school they are in. This article aims to assess the teaching capacity of counselors and targeted training capabilities. The counselor’s ability is reflected in the teaching of teaching, completing teaching objectives and cultivates student’s hobbies. In this paper, through investigating the cultivation methods of different industries, the cultivation of counselor’s ability is to assess consensus and establish multilevel teachers’ teaching capacity cultivation system. The results show that the system can effectively improve the teaching capacity of young teachers and increase 20% of student’s satisfaction; however, it also shows that the professional competence of counselors still needs to be improved.

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Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern

Cited by 23 publications

References 43 publications

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

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