High-dimensional Feature Space Research Articles

Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space.

We consider the problem of nonparametric classification from a high-dimensional input vector (small n large p problem). To handle the high-dimensional feature space, we propose a random projection (RP) of the feature space followed by training of a neural network (NN) on the compressed feature space. Unlike regularization techniques (lasso, ridge, etc.), which train on the full data, NNs based on compressed feature space have significantly lower computation complexity and memory storage requirements. Nonetheless, a random compression-based method is often sensitive to the choice of compression. To address this issue, we adopt a Bayesian model averaging (BMA) approach and leverage the posterior model weights to determine: 1) uncertainty under each compression and 2) intrinsic dimensionality of the feature space (the effective dimension of feature space useful for prediction). The final prediction is improved by averaging models with projected dimensions close to the intrinsic dimensionality. Furthermore, we propose a variational approach to the afore-mentioned BMA to allow for simultaneous estimation of both model weights and model-specific parameters. Since the proposed variational solution is parallelizable across compressions, it preserves the computational gain of frequentist ensemble techniques while providing the full uncertainty quantification of a Bayesian approach. We establish the asymptotic consistency of the proposed algorithm under the suitable characterization of the RPs and the prior parameters. Finally, we provide extensive numerical examples for empirical validation of the proposed method.

Feature selection via a multi-swarm salp swarm algorithm

<abstract><p>Feature selection (FS) is a promising pre-processing step before performing most data engineering tasks. The goal of it is to select the optimal feature subset with promising quality from the original high-dimension feature space. The Salp Swarm Algorithm (SSA) has been widely used as the optimizer for FS problems. However, with the increase of dimensionality of original feature sets, the FS problems propose significant challenges for SSA. To solve these issues that SSA is easy to fall into local optimum and have poor convergence performance, we propose a multi-swarm SSA (MSSA) to solve the FS problem. In MSSA, the salp swarm was divided into three sub-swarms, the followers updated their positions according to the optimal leader of the corresponding sub-swarm. The design of multi-swarm and multi-exemplar were beneficial to maintain the swarm diversity. Moreover, the updating models of leaders and followers were modified. The salps learn from their personal historical best positions, which significantly improves the exploration ability of the swarm. In addition, an adaptive perturbation strategy (APS) was proposed to improve the exploitation ability of MSSA. When the swarm stagnates, APS will perform the opposition-based learning with the lens imaging principle and the simulated binary crossover strategy to search for promising solutions. We evaluated the performance of MSSA by comparing it with 14 representative swarm intelligence algorithms on 10 well-known UCI datasets. The experimental results showed that the MSSA can obtain higher convergence accuracy with a smaller feature subset.</p></abstract>

A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification

In text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.

Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features

Sensor fault diagnosis is essential to guaranteeing the safety of lithium-ion batteries. To address the general drawbacks of the existing diagnosis methods, including the difficulty in determining the threshold, inability to handle multiple faulty sensors concurrently, and limited capacity in identifying fault modes, a multi-sensor multi-mode fault diagnosis method for lithium-ion battery packs is proposed. The proposed method utilizes time series and discriminative features to accomplish sensor-specific fault detection and fault mode identification. First, a total of 18 general time series features are extracted to characterize the measurements of each sensor during each charge–discharge cycle. Principal component analysis is then used to reduce the high-dimensional feature space to a two-dimensional space, such that fault detection can be carried out with the α-hull algorithm. For the detected faulty samples, a two-layer identification algorithm is designed based on three discriminative features, namely, correlation coefficient, impulse factor, and Hurst coefficient, to identify the specific fault modes. The diagnostics can decouple the information from different types of sensors so that the proposed method can effortlessly isolate current, voltage, and temperature sensors that are concurrently experiencing faults. Ultimately, experimental results from three scenarios, including simultaneous failure of multiple sensors, substantiate the effectiveness and feasibility of the proposed method.

Space-feature measures on meshes for mapping spatial transcriptomics

Advances in the development of largely automated microscopy methods such as MERFISH for imaging cellular structures in mouse brains are providing spatial detection of micron resolution gene expression. While there has been tremendous progress made in the field of Computational Anatomy (CA) to perform diffeomorphic mapping technologies at the tissue scales for advanced neuroinformatic studies in common coordinates, integration of molecular- and cellular-scale populations through statistical averaging via common coordinates remains yet unattained. This paper describes the first set of algorithms for calculating geodesics in the space of diffeomorphisms, what we term space-feature-measure LDDMM, extending the family of large deformation diffeomorphic metric mapping (LDDMM) algorithms to accommodate a space-feature action on marked particles which extends consistently to the tissue scales. It leads to the derivation of a cross-modality alignment algorithm of transcriptomic data to common coordinate systems attached to standard atlases.We represent the brain data as geometric measures, termed as space-feature measures supported by a large number of unstructured points, each point representing a small volume in space and carrying a list of densities of features elements of a high-dimensional feature space. The shape of space-feature measure brain spaces is measured by transforming them by diffeomorphisms. The metric between these measures is obtained after embedding these objects in a linear space equipped with the norm, yielding a so-called “chordal metric”.

Quantum intelligence in medicine: Empowering thyroid disease prediction through advanced machine learning

AbstractThe medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)‐based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F‐1‐score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir.

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information.

The intuitionistic fuzzy twin support vector machine (IFTSVM) merges the idea of the intuitionistic fuzzy set (IFS) with the twin support vector machine (TSVM), which can reduce the negative impact of noise and outliers. However, this technique is not suitable for multi-class and high-dimensional feature space problems. Furthermore, the computational complexity of IFTSVM is high because it uses the membership and non-membership functions to build a score function. We propose a new version of IFTSVM by using relative density in-formation. This idea approximates the probability density distribution in multi-dimensional continuous space by computing the K-nearest-neighbor distance of each training sample. Then, we evaluate all the training points by a one-versus-one-versus-rest strategy to construct the k-class classification hyperplanes. A coordinate descent system is utilized to reduce the computational complexity of the training. The bootstrap technique with a 95% confidence interval and Friedman test are conducted to quantify the significance of the performance improve-ments observed in numerical evaluations. Experiments on 24 bench-mark datasets demonstrate the proposed method produces promising results as compared with other support vector machine models reported in the literature.

A Large-Class Few-Shot Learning Method Based on High-Dimensional Features

Large-class few-shot learning has a wide range of applications in many fields, such as the medical, power, security, and remote sensing fields. At present, many few-shot learning methods for fewer-class scenarios have been proposed, but little research has been performed for large-class scenarios. In this paper, we propose a large-class few-shot learning method called HF-FSL, which is based on high-dimensional features. Recent theoretical research shows that if the distribution of samples in a high-dimensional feature space meets the conditions of compactness within the class and the dispersion between classes, the large-class few-shot learning method has a better generalization ability. Inspired by this theory, the basic idea is use a deep neural network to extract high-dimensional features and unitize them to project the samples onto a hypersphere. The global orthogonal regularization strategy can then be used to make samples of different classes on the hypersphere that are as orthogonal as possible, so as to achieve the goal of sample compactness within the class and the dispersion between classes in high-dimensional feature space. Experiments on Omniglot, Fungi, and ImageNet demonstrate that the proposed method can effectively improve the recognition accuracy in a large-class FSL problem.

Investigating Thrombosis Risk Factors in Sickle Cell Disease Utilizing Archetypal Analysis

Background: Thrombosis in sickle cell disease (SCD) affects both the arterial and venous vasculature with devastating consequences. Individuals with SCD have an increased risk of venous thromboembolism (VTE). The risks are multifactorial with prior studies identifying both VTE-specific and SCD-specific risk factors. Machine learning (ML) offers the potential to detect non-linear relationships in complex multifactorial diseases and has successfully predicted outcomes in deeply phenotyped patients. We investigated whether archetypal analysis (AA), a method of ML would identify clinical and laboratory factors associated with arteriovenous thrombosis in patients with SCD. Methods: We retrospectively analyzed a prospective longitudinal cohort of patients with SCD (NCT00011648) studied between 2000-2017. A prior history of VTE and/or stroke at the time of study entry as captured by the electronic medical record was used to define index “cases”. SCD patients without a history of thrombosis were “controls”. The demographic and clinical characteristics of cases and controls were compared using Wilcoxon rank-sum test for continuous and Fisher's exact test for categorical variables. Unsupervised AA was used to identify patterns of clinical and laboratory test characteristics within the entire cohort of patients including cases and controls. AA identifies patterns are on the convex hull that encloses all patients in the cohort in the high-dimensional feature space. Once specific archetypes were identified, the frequency of each archetype was determined within cases and controls. In addition, we describe the age and laboratory characteristics of the archetypes that were significantly more prevalent in cases as compared to controls. Results: The cohort consisted of 633 out of 862 patients that had their whole exome sequenced. A combined total of 160 arterial and venous thrombosis events was detected in 148/633 patients (24%) with half (n=79) attributable to arterial stroke and half (n=81) attributable to VTE [Table 1]. A small proportion of patients had both arterial and venous thrombosis (n=12). The median age (31 years, p=0.94) and sex were similar between the cases (n=148) and controls (n=485). The distribution of genotypes was similar between the two groups (p=0.09). A significantly higher proportion of patient with thrombosis had a prior history of blood transfusion (p<0.0001) and history of insertion of a central venous access device (p=<0.0001) [Table 1]. History of medical management with hydroxyurea was similar among the cases and controls (p=0.70). Compared to controls, the cases had elevated laboratory parameters including AST levels (p=0.02), and serum ferritin (p<0.0001), while HbS percentages were significantly lower (p<0.0001). Based on the minimum error in matrix factorization, a total of 15 archetypes were identified in the cohort [Figure 1]. Each patient was decomposed as a weighted combination of the 15 archetypes. The contributing archetypes for each patient were those archetypes that have non-zero weights. Among those 15 archetypes, archetypes 9, 14 and 15 were significantly more enriched among the cases compared to controls (p<0.02) [Figure 1]. We observed elevated serum ferritin levels and low levels of HbS across the three archetypes, which are consistent with the previously identified risk factors [Figure 1]. Conclusions: Machine-identified archetypes are objective and reproducible feature representations. Archetype analysis-based machine learning appears to hold promise to identify both traditional VTE and SCD-specific risks for clinical thrombosis. Future studies will utilize archetypal analysis to investigate associations between clinical, laboratory and genetic markers for thrombosis and assess their reproducibility using validation cohorts.

Performance evaluation of KPCA pre-imaging methods for speech signal denoising

<p class="Abstract">Kernel principal component analysis (KPCA) has gained wider interest amongst the researchers in nonlinear dimensionality reduction, data compression, feature extraction, and denoising applications. In KPCA, data from low dimensional input space implicitly mapped to higher dimensional feature space where linear PCA is enforced. However, for denoising application data need to invert back to input space, which is impossible and known as pre-imaging problems. Over recent years, several pre-imaging methods have proposed each with its benefits and disadvantages. In this paper, we evaluated the performance for selected pre-imaging methods for denoising speech signal, whose intelligibility and quality degraded by background noises. Further, we extend our work by comparing the performance of these pre-imaging methods by objective evaluation of denoised speech signal on generated toy examples and NOIZUES database.</p>

Novel machine learning model to improve performance of an early warning system in hospitalized patients: a retrospective multisite cross-validation study

Threshold-based early warning systems (EWS) are used to predict adverse events (Aes). Machine learning (ML) algorithms that incorporate all EWS scores prior to an event may perform better in hospitalized patients. The deterioration index (DI) is a proprietary EWS. A threshold of DI >60 is used to predict a composite AE: all-cause mortality, cardiac arrest, transfer to intensive care, and evaluation by the rapid response team in practice. The DI scores were collected for adult patients (≥18 y-o) hospitalized on medical or surgical services during 8-23-2021 to 3-31-2022 from four different Mayo Clinic sites in the United States. A novel ML model was developed and trained on a retrospective cohort of hospital encounters. DI scores were represented in a high-dimensional space using random convolution kernels to facilitate training of a classifier and the area under the receiver operator characteristics curve (AUC) was calculated. Multiple time intervals prior to an AE were analyzed. A leave-one-out cross-validation protocol was used to evaluate performance across separate clinic sites. Three different classifiers were trained on 59,617 encounter-derived DI scores in high-dimensional feature space and the AUCs were compared to two threshold models. All three tested classifiers improved the AUC over the threshold approaches from 0.56 and 0.57 to 0.76, 0.85 and 0.94. Time interval analysis of the top performing classifier showed best accuracy in the hour before an event occurred (AUC 0.91), but prediction held up even in the 12h before an AE (AUC 0.80at minus 12h, 0.81at minus 9h, 0.85at minus 6h, and 0.88at minus 3h before an AE). Multisite cross-validation using leave-one-out approach on data from four different clinical sites showed broad generalization performance of the top performing ML model with AUC of 0.91, 0.91, 0.95, and 0.91. A novel ML model that incorporates all the longitudinal DI scores prior to an AE in a hospitalized patient performs better at outcome prediction than the currently used threshold model. The use of clinical data, a generalized ML technique, and successful multisite cross-validation demonstrate the feasibility of our model in clinical implementation. No funding to report.

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

NIMG-59. RADIOMIC FEATURE CLUSTER ANALYSIS OF IDH-MUTANT GLIOMA SUBTYPES

Abstract INTRODUCTION IDH-mutant glioma subtypes share similarities on MRI and histopathology but have distinct differences in survival based on their molecular subtypes. Radiomic features provide novel analysis of glioma imaging phenotypes that may not appear in clinical evaluations. We evaluated whether clustering of radiomic features of IDH-mutant gliomas could identify different subtypes based on the 2021 WHO classification. METHODS We extracted up to 5,763 radiomic features per case from pretreatment FLAIR and T1 post-contrast tumor segmentations of 206 patients with IDH-mutant gliomas WHO grade 2-4 using a standardized Pyradiomics 3.1.0 pipeline. Segmentations were performed using a semiautomatic workflow with a UNETR algorithm trained on the BRaTS 2021 dataset. To visualize high-dimensional feature space, we evaluated the following unsupervised clustering algorithms to differentiate tumor subtypes: PCA, t-SNE, UMAP, and PHATE. We performed redundancy reduction by calculating a Pearson correlation-matrix, and removed highly correlated features above a threshold of 0.7. Significant features were selected using the L1-norm regularization (LASSO). RESULTS The dataset of 206 patients included 19 grade 4, 28 grade 3, and 36 grade 2 astrocytomas as well as 23 grade 3 and 58 grade 2 oligodendrogliomas. The UNETR algorithm performed with a 0.85 Dice score on the segmentations. Clustering of the extracted 2D radiomic features from FLAIR and T1postgad using PCA, tSNE, UMAP, and PHATE did not identify distinct classes. To further analyze the two tumor entities, we applied XGBOOST, RandomForest, and KNN classifiers and found them to have AUCs < 0.5. CONCLUSIONS We showed that the spectrum of imaging features of IDH-mutant gliomas has a significant overlap on FLAIR and T1 post-contrast imaging. We propose that standard anatomical imaging does not provide sufficient information on these two sequences. To further identify specific subtypes of IDH-mutant gliomas, additional sequences, such as T2, ADC, SWI, and T1, can provide additional information.

Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models

Various privacy-preserving frameworks that respect the individual’s privacy in the analysis of data have been developed in recent years. However, available model classes such as simple statistics or generalized linear models lack the flexibility required for a good approximation of the underlying data-generating process in practice. In this paper, we propose an algorithm for a distributed, privacy-preserving, and lossless estimation of generalized additive mixed models (GAMM) using component-wise gradient boosting (CWB). Making use of CWB allows us to reframe the GAMM estimation as a distributed fitting of base learners using the L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_2$$\\end{document}-loss. In order to account for the heterogeneity of different data location sites, we propose a distributed version of a row-wise tensor product that allows the computation of site-specific (smooth) effects. Our adaption of CWB preserves all the important properties of the original algorithm, such as an unbiased feature selection and the feasibility to fit models in high-dimensional feature spaces, and yields equivalent model estimates as CWB on pooled data. Next to a derivation of the equivalence of both algorithms, we also showcase the efficacy of our algorithm on a distributed heart disease data set and compare it with state-of-the-art methods.

Kernel Geometric Mean Metric Learning

Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over other existing metric learning technologies. However, addressing the nonlinear problem is not effective enough. The kernel method is an effective method to solve nonlinear problems. Therefore, a kernel geometric mean metric learning (KGMML) algorithm is proposed. The basic idea is to transform the input space into a high-dimensional feature space through nonlinear transformation, and use the integral representation of the weighted geometric mean and the Woodbury matrix identity in new feature space to generalize the analytical solution obtained in the GMML algorithm as a form represented by a kernel matrix, and then the KGMML algorithm is obtained through operations. Experimental results on 15 datasets show that the proposed algorithm can effectively improve the accuracy of the GMML algorithm and other metric algorithms.

The Application of Online-Offline Mixed Teaching Mode in Computer Teaching in Colleges and Universities in the Context of Internet+

Abstract This paper uses a deep learning algorithm to extract training samples for hybrid teaching resources in the context of Internet+. The support vector machine establishes a linear regression function, and a nonlinear function is used to map the samples to a high-dimensional feature space for solving. To optimize the training problem, a deep neural network is employed to construct a neuronal structure model and calculate the loss function. The text vectorization method was selected as the input layer processing of the convolutional neural network, and the index scores of the hybrid teaching model in the context of Internet+ were all 6 and above, with the highest score reaching 10. Therefore, online and offline hybrid teaching modes can provide a more flexible and optimized teaching method and promote resource sharing and optimization.

Fault Diagnosis of Analog Circuits Based on The Sailfish Algorithm Optimized SKELM

In view of the long training time for the optimization of the network model parameters of the SELM and the uncertainty of the model generalization ability, this paper proposes an analog circuit fault diagnosis model based on the sailfish algorithm to optimize the stacked kernel extreme learning machine(SKELM). This model introduces a kernel function to build a multi-layer KELM, which can improve the generalization ability and learning speed of the feedforward neural network. The weights of each layer of SKELM are obtained through the automatic encoder training based on the KELM. Since KELM-AE does not need to set initial parameters, the training speed is improved. However, the kernel parameters and regularization coefficients of KELM-AE are set manually, so the sailfish optimizer (SFO) is used to optimize these two parameters, and then the optimal SKELM model is built through layer by layer training. Finally, the Leap frog filter circuit is used as the simulation experiment circuit, and further compared with the optimized SELM. The results show that KELM-AE has strong generalization ability, and it can map fault features to high-dimensional feature space through nonlinear mapping without extracting fault features separately, thus improving the classification accuracy.

Lifting-based variational multiclass segmentation algorithm: Design, convergence analysis, and implementation with applications in medical imaging

We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determines multiple functions that encode the segmentation regions by minimizing an energy functional combining information from different channels. Multichannel image data can be obtained by lifting the image into a higher dimensional feature space using specific multichannel filtering or may already be provided by the imaging modality under consideration, such as an RGB image or multimodal medical data. Experimental results show that the proposed method performs well in various scenarios. In particular, promising results are presented for two medical applications involving classification of brain abscess and tumor growth, respectively. As main theoretical contributions, we prove the existence of global minimizers of the proposed energy functional and show its stability and convergence with respect to noisy inputs. In particular, these results also apply to the special case of binary segmentation, and these results are also novel in this particular situation.

High-Dimensional Feature Space for Diabetes Diagnosis and Identification of Diabetic-Sensitive Features in Ayurvedic Nadi Signals

Nadi-based disease diagnosis is a traditional art in Ayurvedic medicine that is an inquisitive yet not widely comprehended subject. A collection of higher dimensional features from a preprocessed Nadi dataset was extracted and analyzed to diagnose diabetes. The t-distributed Stochastic Neighbor Embedding was used to visualize the higher dimensional feature space in 2-D. The linear dimensionality reduction method of Principal Component Analysis and several linear and nonlinear classifiers were tested on the reduced feature space in identifying diabetes. The key outcomes of this paper are the ability to reduce the feature space by 73.33% while retaining a classification accuracy of 95.4%, identifying age as a compounding factor in diagnosis, and extracting the diabetes-sensitive features with eigenvalue loading.