Interdistrict Migration in Great Britain 1980–81: A Multistream Model with a Commuting option

I Gordon

doi:10.1068/a200907

Interdistrict Migration in Great Britain 1980–81: A Multistream Model with a Commuting option

Environment and Planning A Economy and Space ◽

10.1068/a200907 ◽

1988 ◽

Vol 20 (7) ◽

pp. 907-924 ◽

Cited By ~ 13

Author(s):

I Gordon

Keyword(s):

Great Britain ◽

Decision Tree ◽

Logistic Function ◽

Extended Model ◽

Heterogeneous Mixture ◽

Geographical Pattern ◽

Residential Move ◽

Stream Model

Migrational flows as recorded in the census are a heterogeneous mixture of sets of movements responding in quite different ways to area characteristics and to the friction of distance. To model such flows requires a multistream approach reflecting the principal options in the decision tree facing prospective residential or workplace movers. In this paper an existing three-stream model of migration is adopted and extended to provide a first representation of district—district flows within Great Britain in 1980–81. The extended model incorporates an option for prospective migrants to choose commuting rather than a residential move after finding a new workplace, and employs a logistic function of distance in the regional or environmental stream where migrational opportunities tend not to be independent. Results are presented for the distribution of flows between local, regional, and national streams and for the geographical pattern of pushes and pulls in each.

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Identification of Mild Cognitive Impairment Subtypes Predicting Conversion to Alzheimer’s Disease Using a Heterogeneous Mixture Learning

10.21203/rs.3.rs-129455/v1 ◽

2020 ◽

Author(s):

Masataka Kikuchi ◽

Kaori Kobayashi ◽

Sakiko Itoh ◽

Kensaku Kasuga ◽

Akinori Miyashita ◽

...

Keyword(s):

Gene Expression ◽

Clinical Trials ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Decision Tree ◽

Brain Atrophy ◽

Heterogeneous Mixture ◽

Tree Model ◽

Conversion Rates ◽

Csf Biomarker

Abstract BackgroundMild cognitive impairment (MCI) is a high-risk condition for conversion to dementias, including Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneity in patterns of pathology, and MCI does not always convert to AD dementia. Detailed subtyping of MCI and accurate prediction of the patients in whom MCI will convert to AD dementia may support new trial designs and enable evaluation of the efficacy of drugs within small numbers of patients during clinical trials. MethodsWe constructed a decision tree model by the heterogeneous mixture learning (HML) method, integrating cerebrospinal fluid (CSF) biomarker data, structural MRI data, APOE genotype data, and a recorded age at examination. The decision tree model was applied to predict conversion to AD dementia and to identify subtypes of MCI. After the test performances of HML models were assessed, MCI subjects were classified into some subtypes based on a decision tree. Then, we characterized each MCI subtype in terms of the degree of CSF biomarker abnormalities and brain atrophy, declines of cognitive functions, and gene expression alterations derived from peripheral blood samples.ResultsWe identified five subtypes of MCI using the HML approach and categorized them into three groups: those similar to CN subjects with low conversion rates; those with intermediate conversion rates; and those similar to patients with AD with high conversion rates. Furthermore, the subtypes with intermediate conversion rates were separated into the subtype with CSF biomarker abnormalities or the subtype with brain atrophy. The results from the CSF inflammation marker and gene expression analysis suggested the occurrence of aberrant inflammatory immune responses in the CSF and blood of the subjects in the subtypes with CSF biomarker abnormalities. ConclusionThe subtypes that were identified in this study exhibited varying conversion rates to AD as well as differing levels of biological features. Focusing on specific subtypes in which conversion to AD can be predicted with the most accuracy could enable more efficient clinical trials to be conducted.

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Instance Selection dengan Naïve Bayes pada Klasifikasi Kanker Serviks

Jurnal Komtika ◽

10.31603/komtika.v5i2.6041 ◽

2021 ◽

Vol 5 (2) ◽

pp. 83-91

Author(s):

Fari Katul Fikriah

Keyword(s):

Decision Tree ◽

Missing Values ◽

Naive Bayes ◽

Healing Process ◽

Logistic Function ◽

Naïve Bayes ◽

Instance Selection ◽

Feature Selection Technique ◽

Deadly Disease ◽

Decision Tree Method

There are several deadly disease for woman, one of which is servical cancer. The growth and development of the disease is very slow, so that treatment if know form the beginning will facilitate the healing process, but conversely unknown cancers from the beginning will become dangereous and deadly disease due to relatively difficult healing. Biopsy action is one way to detect the presence of cancer. In the previous study, classification of cervical cancer had the bighest accuracy value of 97,515% using the decision tree method of several feature selection technique. for this reason, this research will use the decision tree or tree C4.5 classification method, logistic function and zeroR which were previously carried out processing with instance selection with Naïve Bayes by reducing the elimination of missing values with the aim of increasing the level of accuracy better than previous studies. C4.5 classification in this study has the most maximum results compared to other classification methods with an accuracy value of 99,69%.

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