AIMC Topic: Logistic Models

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Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification.

Sensors (Basel, Switzerland)
Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a varie...

Analysis on Risk Characteristics of Traffic Accidents in Small-Spacing Expressway Interchange.

International journal of environmental research and public health
Many small-spacing interchanges (SSI) appear when the density of the expressway interchanges increases. However, the characteristics of traffic accidents in SSI have not been explained clearly. Therefore, this paper systematically takes the G3001 exp...

Research on the Audit Prediction Model of "Special Bonds + PPP" Project based on Machine Learning.

Computational intelligence and neuroscience
This paper aims at the whole-process tracking audit problem of "special bonds + PPP" mode (hereinafter referred to as "special bonds + PPP") in public infrastructure construction projects and establishes an audit evaluation prediction model based on ...

Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior databases.

Research in social & administrative pharmacy : RSAP
BACKGROUND: The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be ...

Predicting malnutrition from longitudinal patient trajectories with deep learning.

PloS one
Malnutrition is common, morbid, and often correctable, but subject to missed and delayed diagnosis. Better screening and prediction could improve clinical, functional, and economic outcomes. This study aimed to assess the predictability of malnutriti...

Machine learning in the loop for tuberculosis diagnosis support.

Frontiers in public health
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyz...

Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model.

Journal of affective disorders
BACKGROUND: Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.

Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of osteoporosis. The model performance on outcome prediction was compared between four machine learning a...

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure.

International journal of cardiology
BACKGROUND: Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and...

Development and external validation of predictive algorithms for six-week mortality in spinal metastasis using 4,304 patients from five institutions.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Historically, spine surgeons used expected postoperative survival of 3-months to help select candidates for operative intervention in spinal metastasis. However, this cutoff has been challenged by the development of minimally inva...