Discriminant Analysis Classifier Research Articles

Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques

Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study

BackgroundFabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid’s molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy.ResultsThe Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy.ConclusionATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

PurposeIn today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.Design/methodology/approachA questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.FindingsThe findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.Originality/valueThis study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry.

To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method. Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10-15 minutes, were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles. Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two-class PLS-DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone. This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2-butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC-IMS could be a viable non-invasive method for monitoring glycaemic states and managing diabetes.

Computerized Principal Component–Canonical Discriminant Analysis Classification Model for Pollen Grain Identification Using Surface-Enhanced Raman Spectroscopy

Pollen identification and quantification are employed in various applications, and studies have been conducted to achieve precise automated pollen recognition to minimize the manual labor and subjectivity associated with human identification. The objective of the study was to evaluate the potential of surface-enhanced Raman spectroscopy (SERS) properties of pollen grain spectra, specifically within the spectral range of 582–1563 cm–1, using the convective assembly combined principal component–canonical discriminant analysis (PC-CDA) classification model. This research aimed to explore the feasibility of using this approach for the automated identification of pollen in the future. This approach facilitates the optimal arrangement of pollen grains and colloidal silver nanoprobes, thereby enhancing SERS spectra reproducibility using the convective assembly sampling method. Pollen grains can be characterized at a microscopic level without the need for purification-based analysis procedures. The findings demonstrate the possibility of rapid and accurate classification of pollen grains with the PC-CDA detection model with a 99.3% sensitivity, 97.6% specificity, and 98.5% accuracy. Research study indicates that the SERS properties of pollen spectra could serve as a promising method for automated pollen identification using the PC–CDA classification model.

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.

Uterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity. We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning

Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.

Wearable Surface Deformation Myography (sDMG) System for Recognition of Locomotion Modes.

This study designs a wearable sensing system for locomotion mode recognition using lower-limb skin surface curvature deformation caused by the morphological changes of musculotendinous complexes and soft tissues. Flexible bending sensors are embedded into stretch pants, enabling curvature deformations of specific skin segments above lower-limb muscle groups to be captured in a noncontact manner. To evaluate the performance of this system, we conducted experiments on eight able-bodied subjects completing seven common locomotive activities, including walking, running, ramp ascending/descending, stair ascending/descending, and standing. The system measured seven channels of deformation signals from two cross-sections on the shank and the thigh. The collected signals were distinguishable across different locomotion modes and exhibited consistency when monitoring steps. Using selected time-domain features and a linear discriminant analysis (LDA) classifier enabled the proposed system to continuously recognize locomotion modes with an average accuracy of 96.5%. Furthermore, the system maintains recognition performance with 95.7% accuracy even after removing and reapplying the sensors. Finally, we conducted comparison experiments to analyze how window length, feature selection, and the number of channels affect recognition performance, providing insights for optimization. We believe that this novel signal platform holds great potential as a valuable supplementary tool in wearable human motion detection, enriching the information diversity for motion analysis, and enabling new possibilities for further advancements and applications in fields including biomedical engineering, textiles, and computer graphics.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

Serum lipidomic profiling of dairy calves fed milk replacers containing animal or vegetable fats

Vegetable fat blends are commonly used as fat sources in milk replacers (MR) for calves, but their composition differs considerably from that of bovine milk fat. The aim of this study was to investigate the serum lipid profile of pre-weaned calves fed twice-daily MR containing 30% fat (% DM). Upon arrival, 30 male Holstein-Friesian calves (BW = 45.6 ± 4.0 kg, age = 2.29 ± 0.8 d) were randomly assigned to 2 experimental diets (n = 15 per treatment): one MR was derived from either vegetable fats (VG; 80% rapeseed and 20% coconut fats) or animal fats (AN; 65% Packer's lard and 35% dairy cream). The 2 MR formulas contained 30% fat, 24% CP, and 36% lactose. Calves were housed indoors in individual pens with ad libitum access to chopped straw and water. Daily milk allowances were 6.0 L from d 1 to 5, 7.0 L from d 6 to 9, and 8.0 L from d 10 to 35, divided into 2 equal meals and prepared at 13.5% solids. An untargeted liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) method was employed to analyze the lipid profiles in the serum of calves sampled from the jugular vein at 35 d of age. In total, 594 lipids were characterized, comprising 25 different lipid classes. Principal component analysis (PCA) showed significant separation between VG and AN, indicating different lipid profiles in the serum. An orthogonal partial least squares discriminant analysis (OPLS-DA) classification model was used to further validate the distinction between the 2 treatment groups. The model exhibited a robust class separation and high predictive accuracy. Using a Volcano plot (fold change threshold ≥1.5 and false discovery rate ≤0.05), it was observed that calves fed AN had higher levels of 39 lipid species in serum than calves fed VG, whereas 171 lipid species were lower in the AN group. Lipid classes, such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), triglycerides (TG), lysophosphatidylcholine (LPC), and lysophosphatidylethanolamine (LPE), were different. In particular, PC and PE were observed at lower levels in calves fed AN, possibly indicating shifts in cell membrane characteristics, intracellular signaling, and liver functions. In addition, a decrease in certain triglyceride (TG) species was observed in calves fed AN, including a decrease in TG species such as TG 36:0 and TG 38:0, possibly related to variations in the content of certain fatty acids (FA) within the AN MR, such as C10:0, C12:0, C14:0, and C18:0 compared with the VG MR. Calves fed AN had lower levels of LPC and LPE, and lyso-phosphatidylinositol (LPI), SM, and phosphatidylinositol (PI) species than calves fed VG, suggesting shifts in lipoprotein and lipid metabolic pathways. In conclusion, these results deepen the understanding of how lipid sources in MR can modulate the serum lipidome profiles of dairy calves.

Designing a Motion-Onset Visual-Evoked Potential-Based Brain–Computer Interface to Control a Computer Game

Brain-computer interface (BCI) establishes an alternative direct communication channel between the brain and computer or other external devices. Motion-onset visual evoked potential (mVEP) related to the individual perception of movement is among synchronous BCI system that utilizes external stimulus. mVEPs stimulation paradigm is like an oddball stimulation paradigm. Researchers have recently been investigating mVEPs to control a computer game. To control a computer game, they used translation stimulus to evoke these potentials in EEG brain signals. In this study, we examine designing a computer game with four different types of stimuli, including translation, contraction, expansion, and rotation. We also used time-domain and wavelet coefficients for the feature extraction. Also, we used different types of linear discriminant analysis (LDA) classification methods, including least absolute shrinkage and selection operator (LASSO), stepwise LDA (SWLDA); moreover, and spatial-temporal discriminant analysis (STDA) for detecting the target stimulus. Our results demonstrate that contraction, expansion, and rotation stimuli, as well as translation, could be employed to control a BCI game. Our results showed that subjects got higher scores, game accuracy control, and information transfer rate using the rotation stimulus with SWLDA/STDA classification methods.

Differential effects of mass-loading the eardrum and stiffening the middle ear on wideband absorbance.

The current work investigated the effects of mass-loading the eardrum on wideband absorbance in humans. A non-invasive approach to mass-loading the eardrum was utilized in which water was placed on the eardrum via ear canal access. The mass-loaded absorbance was compared to absorbance measured for two alternative middle ear states: normal and stiffened. To stiffen the ear, subjects pressurized the middle ear through either exsufflation or insufflation concurrent with Eustachian tube opening. Mass-loading the eardrum was hypothesized to reduce high-frequency absorbance, whereas pressurizing the middle ear was hypothesized to reduce low- to mid-frequency absorbance. Discriminant linear analysis classification was performed to evaluate the utility of absorbance in differentiating between conditions. Water on the eardrum reduced absorbance over the 0.7- to 6-kHz frequency range and increased absorbance at frequencies below approximately 0.5 kHz; these changes approximated the pattern of changes reported in both hearing thresholds and stapes motion upon mass-loading the eardrum. Pressurizing the middle ear reduced the absorbance over the 0.125- to 4-kHz frequency range. Several classification models based on the absorbance in two- or three-frequency bands had accuracy exceeding 88%.

Virtual reconstruction and geometric morphometric analysis of the Kocabaş hominin fossil from Turkey: Implications for taxonomy and evolutionary significance

The Kocabaş specimen comes from a travertine quarry near the homonymous village in the Denizli basin (Turkey). The specimen comprises three main fragments: portions of the right and left parietal and left and right parts of the frontal bone. The fossil was assumed to belong to the Homo erectus s.l. hypodigm by some authors, whereas others see similarities with Middle Pleistocene fossils (Broken Hill 1/Kabwe, Bodo, or Ceprano). Here, we present the first attempt to make a complete reconstruction of the missing medial portion of the frontal bone and a comprehensive geometric morphometric analysis of this bone. We restored the calotte by aligning and mirroring the three preserved fragments. Afterward, we restored the missing portion by applying the thin-plate spline interpolation algorithm of target fossils onto the reconstructed Kocabaş specimen. For the geometric morphometric analyses, we collected 80 landmarks on the frontal bone (11 osteometric points, 14 bilateral curve semilandmarks, and 41 surface semilandmarks). The comparative sample includes 21 fossils from different chronological periods and geographical areas and 30 adult modern humans from different populations. Shape analyses highlighted the presence in Kocabaş of features usually related to Middle Pleistocene Homo, such as a developed supraorbital torus associated with a relatively short frontal squama and reduced post-toral sulcus. Cluster analysis and linear discriminant analysis classification procedure suggest Kocabaş being part of the same taxonomic unit of Eurasian and African Middle Pleistocene Homo. In light of our results, we consider that attributing the Kocabaş hominin to H. erectus s.l. may be unwarranted. Results of our analyses are compatible with different evolutionary scenarios, but a more precise chronological framework is needed for a thorough discussion of the evolutionary significance of this specimen. Future work should clarify its geological age, given uncertainties regarding its stratigraphic provenance.

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.

Bacterial Disease Detection of Cherry Plant Using Deep Features

Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.

PLS Discriminant Analysis for Classification of Caries Level

PLS Discriminant Analysis for Classification of Caries Level

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.