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multinomial logistic regression algorithm for the classification of patients with parkinsonisms

EJNMMI RESEARCH 2025年第1期15卷 1-14页

作者： Stokelj, Eva Rus, Tomaz Jamsek, Jan Trost, Maja Simoncic, Urban Univ Ljubljana Fac Math & Phys Jadranska ul 19 Ljubljana 1000 Slovenia Univ Med Ctr Ljubljana Dept Neurol Zaloska Cesta 2 Ljubljana 1000 Slovenia Univ Med Ctr Ljubljana Dept Nucl Med Zaloska Cesta 7 Ljubljana 1000 Slovenia Univ Ljubljana Fac Med Vrazov Trg 2 Ljubljana 1000 Slovenia Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia

BackgroundAccurate differential diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping early symptoms and high rates of misdiagnosis. To improve the diagnostic accuracy, we developed an integrated classification algorithm using multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) applied to 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images. In this novel classification approach, SSM/PCA is applied to FDG-PET brain images of patients with various parkinsonisms, which are compared against the constructed undetermined images. This process involves spatial normalization of the images and dimensionality reduction via PCA. The resulting principal components are then used in a multinomial logistic regression model, which generates disease-specific topographies that can be used to classify new patients. The algorithm was trained and optimized on a cohort of patients with neurodegenerative parkinsonisms and subsequently validated on a separate cohort of patients with *** Area Under the Curve (AUC) values were the highest for progressive supranuclear palsy (PSP) (AUC = 0.95), followed by Parkinson's disease (PD) (AUC = 0.93) and multiple system atrophy (MSA) (AUC = 0.90). When classifying the patients based on their calculated probability for each group, the desired tradeoff between sensitivity and specificity had to be selected. With a 99% probability threshold for classification into a disease group, 82% of PD patients, 29% of MSA patients, and 77% of PSP patients were correctly identified. Only 5% of PD, 6% of MSA and 6% of PSP patients were misclassified, whereas the remaining patients (13% of PD, 65% of MSA and 18% of PSP) are undetermined by our classification *** to existing algorithms, this approach offers comparable accuracy and reliability in diagnosing PD, MSA, and PSP with no need of healthy control images. It can also disti

关键词： Parkinsonisms Metabolic brain pattern multinomial logistic regression FDG-PET SSM/PCA

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Class-Distributed Learning for multinomial logistic regression with High Dimensional Features and a Large Number of Classes

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2025年第1期34卷 175-186页

作者： Wu, Shuyuan Zhou, Jing Xu, Ke Wang, Hansheng Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Renmin Univ China Ctr Appl Stat Sch Stat Beijing Peoples R China Univ Int Business & Econ Sch Stat Beijing Peoples R China Peking Univ Guanghua Sch Management Beijing Peoples R China

Estimating a high-dimensional multinomial logistic regression model with a larger number of categories is of fundamental importance but it presents two challenges. Computationally, it leads to heavy computation cost. Statistically, it suffers unsatisfactory statistical efficiency. Therefore, how to solve this problem in a computationally and statistically efficient way is of great interest. To tackle these challenges, we have developed a new class-distributed learning algorithm with a rank-reducible coefficient structure. The key innovation here is piecing together two important techniques for distributed computing and improved statistical efficiency. The two techniques are, respectively, dimension reduction and a circular-structured working model. Dimension reduction effectively alleviates the curse of dimensionality due to high dimensional features. A circular-structured working model allows the use of a class-distributed algorithm for distributed computing. To support our new methodology, we develop rigorous asymptotic theory and present extensive numerical experiments. Supplementary materials for this article are available online.

关键词： Circular-structured working model Class-distributed learning High dimensional feature multinomial logistic regression Rank reducible structure

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Fuzzy multinomial logistic regression Analysis and Its Applications to Sovereign Credit Ratings

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International Journal of Fuzzy Systems 2025年 1-16页

作者： Yoon, Jin Hee Koo, Yoo Young Lee, Wonkyung Kim, Dae Jong Department of Mathematics and Statistics Sejong University Seoul 05006 South Korea University College Yonsei University Incheon 21983 South Korea Department of Chinese Trade and Commerce Sejong University Seoul 05006 South Korea Department of Business and Administration Sejong University Seoul 05006 South Korea

In this study, we propose both a multinomial logistic model and a fuzzy multinomial logistic model that utilize the transformation of variable techniques to simplify the expression of the logistic model. For this purpose, least-squares estimation (LSE) and fuzzy least-squares estimation (FLSE) have been employed. Our primary objective is to enhance the predictive capabilities of multi-class classification models, specifically for forecasting sovereign credit ratings. This research is driven by the need for advanced classification methods using multinomial logistic models that offer flexible reference class prediction, addressing the limitations of traditional logistic techniques in achieving consistent accuracy across diverse classes. We introduce four innovative classification methods for the multinomial logistic model and five for the fuzzy multinomial logistic model, applying them to a comprehensive dataset of sovereign credit ratings from 61 countries, spanning 1995 to 2022, sourced from Moody’s. Our proposed methods outperform conventional logistic techniques and maintain consistent predictive accuracy across a variety of classes when compared to traditional logistic classification methods and machine learning techniques such as decision trees, random forests, and support vector machines. The significance of our findings lies in the broad applicability of these methods beyond financial data;they can be adapted for any context that requires reliable multi-class classification. This advancement holds potential for improving decision-making processes in fields such as economics, risk management, and policy analysis by providing a more consistent and accurate predictive framework. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

关键词： Classification Credit ratings Fuzzy multinomial logistic regression Machine learning multinomial logistic regression

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multinomial logistic regression with soil diagnostic features and land surface parameters for soil mapping of Latium (Central Italy)

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GEODERMA 2019年 352卷 385-394页

作者： Piccini, Chiara Marchetti, Alessandro Rivieccio, Rosa Napoli, Rosario CREA Council Agr Res & Econ Res Ctr Agr & Environm Via Navicella 2-4 I-00184 Rome Italy Molise Univ Dept Biosci & Terr Forestry Labs I-86090 Pesche IS Italy

One of the main challenges in traditional soil mapping is the identification of land components (LCs) - suitable combinations of morphology, lithology and land use - representing a fundamental step in the definition of soil typological units. LCs are traditionally used by pedologists to correlate different soils and to identify the relationships between soil and geography. The recognition of the various soil characteristics for LCs definition is usually performed considering a number of classes for each feature (e.g. slope classes), defined a priori at a national scale. Such classes tend by nature to generalize and to flatten the actual local variability. Moreover, when dealing with large areas, taking into account a very fragmented layout is very difficult. To reduce subjectivity in interpretation, the choice of features defining soil associated LCs should be performed in an automatic or semi-automatic way. Digital Soil Mapping techniques can help to overcome these limits. In this work we propose a procedure to define the soil LCs for the territory of Latium administrative region (Central Italy), starting from a dataset of about 1500 fully described and analysed soil profiles and associated land surface parameters. The main soil diagnostic characteristics - depth, internal drainage, topsoil and subsoil texture, gravel, organic carbon content, cation exchange capacity, calcium carbonate content - were used, together with auxiliary information derived from existing thematic maps (geology, land use, pedoclimate), and from Digital Elevation Model (geomorphometric parameters). Some indices derived from remotely sensed high resolution images were also introduced to improve the estimate in areas with uniform land characteristics (i.e. flat alluvial valleys and coastal plains). Classes for the measured characteristics were defined based on their frequency distribution and of the WRB classification diagnostic thresholds. The covariates were chosen by an ANOVA, and a Princip

关键词： Land components Digital soil mapping multinomial logistic regression Soil diagnostic features Latium Central Italy

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multinomial logistic regression-based feature selection for hyperspectral data

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期14卷 214-220页

作者： Pal, Mahesh Natl Inst Technol Dept Civil Engn Kurukshetra 136119 Haryana India

This paper evaluates the performance of three feature selection methods based on multinomial logistic regression, and compares the performance of the best multinomial logistic regression-based feature selection approach with the support vector machine based recurring feature elimination approach. Two hyperspectral datasets, one consisting of 65 features (DAIS data) and other with 185 features (AVIRIS data) were used. Result suggests that a total of between 15 and 10 features selected by using the multinomial logistic regression-based feature selection approach as proposed by Cawley and Talbot achieve a significant improvement in classification accuracy in comparison to the use of all the features of the DAIS and AVIRIS datasets. In addition to the improved performance, the Cawley and Talbot approach does not require any user-defined parameter, thus avoiding the requirement of a model selection stage. In comparison, the other two multinomial logistic regression-based feature selection approaches require one user-defined parameter and do not perform as well as the Cawley and Talbot approach in terms of (i) the number of features required to achieve classification accuracy comparable to that achieved using the full dataset, and (ii) the classification accuracy achieved by the selected features. The Cawley and Talbot approach was also found to be computationally more efficient than the SVM-RFE technique, though both use the same number of selected features to achieve an equal or even higher level of accuracy than that achieved with full hyperspectral datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Feature selection multinomial logistic regression Support vector machines Classification accuracy

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multinomial logistic regression on Markov Chains for Crop Rotation Modelling

Multinomial Logistic Regression on Markov Chains for Crop Ro...

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15th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU)

作者： Paton, Lewis Troffaes, Matthias C. M. Boatman, Nigel Hussein, Mohamud Hart, Andy Univ Durham Durham DH1 3HP England Food & Environm Res Agcy York N Yorkshire England

ISBN: (纸本)9783319088525;9783319088518

Often, in dynamical systems such as farmer's crop choices, the dynamics are driven by external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary Markov chain, where the transition probabilities are multinomial logistic functions of such external factors. We extend previous work to investigate the problem of estimating the parameters of the multinomial logistic model from data. We use conjugate analysis with a fairly broad class of priors, to accommodate scarcity of data and lack of strong prior expert opinion. We discuss the computation of bounds for the posterior transition probabilities. We use the model to analyse some scenarios for future crop growth.

关键词： multinomial logistic regression Markov chain robust Bayesian conjugate maximum likelihood crop

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multinomial logistic regression Model for Predicting Driver's Drowsiness Using Behavioral Measures

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Procedia Manufacturing 2015年 3卷 2426-2433页

作者： Murata, Atsuo Fujii, Yoshito Naitoh, Kensuke Department of Intelligent Mechanical Systems Graduate School of Natural ScienSce and Technology Okayama University 3-1-1 Tsushimanaka Kita-ward Okayama 700-8530 Japan

The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers’ subjective drowsiness. Behavioral measures included neck vending angle (horizontal and vertical), back pressure, foot pressure, COP (Center of Pressure) movement on sitting surface, and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where physiological and behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods (correlation coefficient-based method and odds ratio-based method) for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five, and six behavioral measures, and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy group. The prediction accuracy differed between the two groups, and the proposed method was effective (the prediction accuracy was significantly higher) especially under the condition where drowsiness was induced to a larger extent. © 2015 The Authors

关键词： Behavioral measures Drowsy driving multinomial logistic regression Physiological measures Prediction accuracy Subjective drowsiness Traffic accident

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multinomial logistic regression PREDICTED PROBABILITY MAP TO VISUALIZE THE INFLUENCE OF SOCIO-ECONOMIC FACTORS ON BREAST CANCER OCCURRENCE IN SOUTHERN KARNATAKA

MULTINOMIAL LOGISTIC REGRESSION PREDICTED PROBABILITY MAP TO...

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ISPRS Technical Commission VIII Symposium

作者： Madhu, B. Ashok, N. C. Balasubramanian, S. JSS Univ Dept Community Med Mysore 570015 Karnataka India JSS Univ Fac Life Sci Mysore 570015 Karnataka India

multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban-rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

关键词： Probability multinomial logistic regression Breast Cancer Socio-economic influence

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Generation of digital soil mapping for Coimbatore districts using multinomial logistic regression approach

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ENVIRONMENTAL EARTH SCIENCES 2024年第24期83卷 677页

作者： Shankar, S. Vishnu Kumaraperumal, R. Radha, M. Kannan, Balaji Patil, S. G. Vanitha, G. Raj, M. Nivas Athira, M. Ananthakrishnan, S. Tamil Nadu Agr Univ Coimbatore India ICAR Natl Rice Res Inst Cuttack India

Digital soil mapping (DSM) is a significant advancement in soil mapping systems, enabling efficient mapping of soil patterns across different temporal and spatial scales. This computer-assisted method surpasses the traditional soil mapping techniques in terms of both compatibility and accuracy. This study employed multinomial logistic regression to map the soil subgroup levels in the Coimbatore district. Primary sample points and Natural Resource Information System (NRIS) database points serve as the dependent variables, while significant covariate layers act as independent variables. The accuracy assessment showed an overall mapping accuracy of 52.58%, with a kappa statistic of 0.50. Additionally, the calculated disagreement measures, including quantity and allocation disagreements, were 21.50% and 25.92%, respectively. The approach provides spatial soil maps at 30 m resolution and was extended for the Coimbatore district of Tamil Nadu, considering the lack of organized high resolution soil maps for operational use. The area statistics calculated from the digital soil map showed that the soil orders Vertisols cover the largest area, accounting for approximately 25.97% (122,630.38 ha) of the total land area. Soil subgroups like Ultic Haplustalfs and Vertic Ustorthents occupy substantial portions of the land, accounting for 9.95% and 9.62% of the total area, respectively. The total land area classified by the map accounts for 427,432.10 ha, i.e., 90.53% of the total land area, of which 44,696.46 ha (9.467%) remains unclassified. The study also presents the statistics on soil order at the block level. These findings provide valuable insights into soil classification, offering a comprehensive understanding of soil distribution and characteristics that support effective decision-making for sustainable land management and agricultural practices.

关键词： Digital soil mapping multinomial logistic regression Principal component analysis Kappa statistics

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A multinomial logistic regression model-based seismic risk assessment method for museum exhibition halls

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JOURNAL OF BUILDING ENGINEERING 2023年 69卷

作者： Yang, Weiguo Zou, Xiaoguang Wang, Meng Liu, Pei Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Beijings Key Lab Struct Wind Engn & Urban Wind Env Beijing Peoples R China

Earthquakes may damage not only museum buildings but also museum artifacts, which has raised concerns worldwide. Although there has been a growing interest in the protection of cultural heritage structures and individual art objects from seismic excitations, there remains a lack of a scientific methodology for the seismic risk assessment of museum exhibition halls where many artifacts are on display. In this work, a seismic risk assessment method for museum exhibition halls is proposed, and the Yuan Dynasty porcelain exhibition hall of the Capital Museum located in Beijing, China, is selected as a representative case for demonstration. Via the proposed method, the seismic fragility analysis of museum exhibition halls is performed on the basis of a developed multinomial logistic regression model. The risk values are then determined from the results of the site hazard and seismic fragility analysis. Finally, a simple fuzzy model is applied to realize the seismic risk assessment according to the calculated risk values. The method considers the detailed geometry of the artifacts, their support conditions, the dynamic response of the showcase, and the floor acceleration amplification of the museum structure. The proposed method can serve as a scientific analytical means by which to assess the seismic risk of museum exhibition halls for the preservation of cultural artifacts.

关键词： Exhibition halls Museum artifacts multinomial logistic regression Seismic fragility Seismic risk assessment

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