Adaptive LASSO logistic regression based on particle swarm optimization for Alzheimer’s disease early diagnosis

2021 ◽  
pp. 104316
Author(s):  
Ruyi Xiao ◽  
Xiaoli Liu ◽  
Hong Qiao ◽  
Xiangwei Zheng ◽  
Yiquan Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Early Diagnosis ◽  
Particle Swarm ◽  
Adaptive Lasso ◽  
Swarm Optimization

scholarly journals Detecting Alzheimer’s Disease by The Decision Tree Methods Based On Particle Swarm Optimization

2020 ◽  
Vol 1641 ◽  
pp. 012025
Author(s):  
R A Saputra ◽  
C Agustina ◽  
D Puspitasari ◽  
R Ramanda ◽  
Warjiyono ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Tree Methods ◽  
Decision Tree Methods

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

2019 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Herlina Herlina ◽  
Ahmad Ridho’i ◽  
Anggie Erma Yunita ◽  
Mega Puja Azhari ◽  
Ade Reynaldi Saputra
Keyword(s):  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Particle Swarm ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Swarm Optimization ◽  
Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

scholarly journals A new Brief computerized cognitive screening battery (CompCogs) for early diagnosis of Alzheimer's disease

2008 ◽  
Vol 2 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Helenice Charchat Fichman ◽  
Ricardo Nitrini ◽  
Paulo Caramelli ◽  
Koichi Sameshima
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Early Diagnosis ◽  
Short Term Memory ◽  
Neuropsychological Test ◽  
Screening Tests ◽  
Cognitive Screening ◽  
Cognitive Components ◽  
Diagnosis Of Dementia

Abstract Screening tests for early diagnosis of dementia are of great clinical relevance. The ideal test set must be brief and reliable, and should probe cognitive components impaired in Alzheimer's disease (AD). Objectives: To develop a new Computerized Cognitive Screening test (CompCogs), and to investigate its validity for the early diagnosis of AD, and evaluate its heuristic value in understanding the processing of information in AD. Methods: The computerized neuropsychological performance battery, originally including six tests, was applied in forty seven patients with probable mild AD and 97 controls matched for age and education. This computerized neuropsychological test battery, developed with MEL Professional, allows control of timing and order of stimuli presentation, as well as recording of response type and latency. A brief-screening version, CompCogs, was selected using the most discriminative neuropsychological test variables derived from logistic regression analysis. Full battery administration lasted about 40 minutes, while the CompCogs took only 15 minutes. Results: CompCogs included the Face test (correct response) and Word and Forms with Short term memory tests (reaction time). CompCogs presented 91.8% sensitivity and 93.6% specificity for the diagnosis of AD using ROC analyses of AD diagnosis probability derived by logistic regression. Conclusions: CompCogs showed high validity for AD early diagnosis and, therefore, may be a useful alternative screening instrument.

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