Histogram Data Research Articles

The Use of Glycan Ensemble Structures and Nonpolar Surface Area Distributions for Correlating with Liquid Chromatography Retention Times.

The separation and structural identification of glycans are of great bioanalytical importance. To obtain a good understanding of the structural flexibility of glycans, replica exchange molecular dynamics (REMD) simulations were used based on AMBER force field calculations to create ensembles of glycan structures. Nonpolar surface area (NPSA) calculations based on continuum solvation (CS) models (Dhakal, R., et al. Int. J. Mass Spectrom. 2021, 461, 116495) were used to quantitatively characterize the polarity of the glycans. Retention times determined by tandem liquid chromatography-mass spectrometry (LC-MS) were correlated with CS-NPSA results obtained from analysis of the investigated glycan ensembles. Three classes of glycans with increasingly complex structures were investigated: linear glycans, fucosylated and sialylated biantennary glycans, and sialylated triantennary glycans. The linear and biantennary structures displayed bimodal distributions in their energies and CS-NPSA values, suggesting two sets of structures, while the more complex triantennary glycans displayed only a single distribution. The peak values of the CS-NPSA distributions (histogram structures) were selected as representatives to be correlated with the experimental retention times. For comparison, the most stable ensemble structures and those obtained from straightforward geometry optimizations were considered, as well. Overall, the histogram structures were found to correlate well with the retention times. In the case of the linear glycans, the CS-NPSA values for all three structural choices correlated very well with the retention times. For the biantennary glycans, the histogram data missed the retention-time ordering in one case but predicted the correct ordering for the triantennary case. Principal component analysis was performed to characterize the main glycan modes of the molecular dynamics.

Anti‐Leakage Method of Sensitive Information of Network Documents Based on Differential Privacy Model

ABSTRACTThis article proposes a method for preventing sensitive information leakage in network documents based on differential privacy models to ensure the security of sensitive information in network documents. Based on the definition of differential privacy and its Laplace mechanism, a differential privacy model was constructed to prevent sensitive information leakage in network documents. Using differential privacy based Laplace mechanism to protect the sensitive information dataset of network documents, and inputting the sensitive information dataset of network documents into the differential privacy model, the sensitive information in network documents is generalized and perturbed, and Laplace noise is added after transformation and compression to prevent leakage. Using roulette wheel sampling technology to sample and sort the original histograms of sensitive data in network documents, using a hierarchical partitioning algorithm to group the sorted histograms, and adding Laplace noise to the groups based on differential confidentiality Laplace mechanism to achieve leak prevention when publishing sensitive messages in network documents. The experimental results show that this method can ensure the security of sensitive information data in network documents and prevent sensitive information leakage in network documents. There is no data loss during the data conversion and compression process of sensitive information. At the same time, the protection effect of sensitive information is less affected by the amount of data, and the loss rate of sensitive information is lower.

Machine Learning for Evaluating Vulnerable Plaque on Coronary Computed Tomography Using Spectral Imaging.

This study aimed to determine whether spectral imaging with dual-energy computed tomography (CT) can improve diagnostic performance for coronary plaque characterization. We conducted a retrospective analysis of 30 patients with coronary plaques, using coronary CT angiography (dual-layer CT) and intravascular ultrasound (IVUS) studies. Based on IVUS findings, patients were diagnosed with either vulnerable or stable plaques. We computed 7 histogram parameters for plaque CT numbers in 120 kVp images and virtual monochromatic images ranging from 40 to 140 keV at 5-keV intervals. A predictive model was developed using histogram data of optimal energy, plaque volume or stenosis, and a combination of both. The model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) using 5-fold cross-validation. Peak diagnostic performances for each histogram parameter were observed at various energy levels (40-110 keV) in the univariate logistic regression model. The histogram model demonstrated optimal diagnostic performance at 65 keV, with an AUC of 0.81. The combined model, incorporating histogram data and plaque volume, achieved an AUC of 0.85, which was similar to the performance of qualitative CT characteristics (AUC=0.88; P=0.70). Spectral imaging with dual-energy CT can enhance the diagnostic performance of machine learning using CT histograms for coronary plaque characterization.

Brain Tumour Detection Using Deep Learning and Image Processing

Abstract —Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

Codebase release 4.0 for RIVET

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

Robust independent validation of experiment and theory: Rivet version 4 release note

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

Image Contrast Soiling Loss Quantification with Multiple Dust Types

The image contrast method is a potentially viable approach for quantifying soiling loss. It involves imaging a surface with intrinsic contrast and applying a mathematical model that correlates the image’s black-to-white ratio with the angle-corrected normal-incidence soiling loss. This method had previously been tested with one type of dust. The objective of this study was to assess the method’s general validity across multiple dust types. Experiments were conducted to measure normal-incidence soiling loss and to capture images of soiling samples over a checker pattern. Histogram data of the images were used to determine the model parameters for each dust type. Using these parameters, the camera-angle-corrected normal-incidence soiling loss could be modeled from the black-to-white ratio, and the model’s predictions agreed closely with the experimental measurements. The findings suggest that the image contrast method can be applied to various dust types and, therefore, can be utilized in different regions around the world.

Prediction of air compressor faults with feature fusion and machine learning

Air compressors are critical for many industries, but early detection of faults is crucial for keeping them running smoothly and minimizing maintenance costs. This contribution investigates the use of predictive machine learning models and feature fusion to diagnose faults in single-acting, single-stage reciprocating air compressors. Vibration signals acquired under healthy and different faulty conditions (inlet valve fluttering, outlet valve fluttering, inlet-outlet valve fluttering, and check valve fault) serve as the study’s input data. Diverse features including statistical attributes, histogram data, and auto-regressive moving average (ARMA) coefficients are extracted from the vibration signals. To identify the most relevant features, the J48 decision tree algorithm is employed. Five lazy classifiers viz. k-nearest neighbor (kNN), K-star, local kNN, locally weighted learning (LWL), and random subspace ensemble K-nearest neighbors (RseslibKnn) are then used for fault classification, each applied to the individual feature sets. The classifiers achieve commendable accuracy, ranging from 85.33% (K-star and local kNN) to 96.00% (RseslibKnn) for individual features. However, the true innovation lies in feature fusion. Combining the three feature types, statistical, histogram, and ARMA, significantly improves accuracy. When local kNN is used with fused features, the model achieves a remarkable 100% classification accuracy, demonstrating the effectiveness of this approach for reliable fault diagnosis in air compressors.

An intelligent bone age assessment model incorporating multilayer superimposed texture enhancement and the China-05 attention mechanism

In the process of intelligent bone age assessment for Chinese ethnicity, generalized convolutional network models are not well targeted in extracting specific features in medical skeletal images and lack specificity in training and predicting skeletal developmental features in different ethnicities. This study aims to propose a hybrid improved deep residual network model, ZH05-DL-ResNet50, focusing on intelligent bone age assessment for Chinese ethnicity. In the process of building the ZH05-DL-ResNet50 model. First, multiple overlapping texture enhancement layers are introduced through combinatorial optimization, which can better characterize the global features of hand bone radiographs while using less texture information, reducing the interference of redundant information and freeing up computational power; second, the China-05′s spatial focusing mechanism is designed so that the model can intelligently focus on the region of interest, efficiently locate and automatically learn key image information; Finally, the model can be used for intelligent bone age assessment of Chinese ethnicity by constructing a 50-layer residual depth network, the superposition enhancement layer and the focusing mechanism are fused to form the ZH05-DL-ResNet50 model, which mitigates the problem of gradient disappearance and explosion caused by the increase of network depth, and reduces the information loss and depletion of the multi-layer texture superposition features in the focus region of interest. The training was performed using the Chinese ethnographic dataset provided and created by Tsinghua Changgeng Hospital, where 10 % of the radiographs were used as the test set and the rest as the training and validation sets. The overall performance of the models was estimated by comparing the average accuracy and loss values of different models, and the overall performance between model training and model design was evaluated using the data distribution (Distribution) and histogram of bias weight distribution (Histogram). The results showed that the average accuracy of the bone age estimation model was 98.1 % and the average error on the test set was 0.312 years. Evaluation of the model visualization and accuracy comparison showed that the model performed well and was more appropriate and accurate than other bone age estimation models. Therefore, the first intelligent bone age assessment model for Chinese ethnicity, ZH05-DL-ResNet50, was created, which differs from the untargeted nature of other intelligent bone age assessment models, and is used to train and predict hand bone radiographs more appropriately and accurately for Chinese ethnicity.

Near-zero distortion in TCSPC at more than one photon per excitation period: experimental validation.

The time-correlated single-photon counting (TCSPC) technique is widely renowned for its capability of reconstructing rapid and weak light signals with exceptional sensitivity and sub-picosecond timing resolution. Unfortunately, the speed of TCSPC has been historically severely limited to avoid a phenomenon known as pileup distortion. For this reason, the count rate of a classic TCSPC acquisition channel is kept below a few percent of the laser excitation rate (usually 1%-5%). In this work, we experimentally validate a novel, to our knowledge, TCSPC theory recently reported that effectively overcomes such a limitation and finally achieves high-speed operation without distortion. Exploiting a single-photon avalanche diode (SPAD), in this paper we show how to acquire additional information about the status of the system at run time, and by combining it with the classic TCSPC data histogram, we report how a count rate of approximately 60% of the excitation frequency with near-zero distortion can indeed be achieved with a commercial system.

Analysis of very small-small theropod footprints data from Imilchil tracksite (Hig Central Atlas. Morocco). A concordance model for a global scale

In the ensemble of theropod footprints from the Imilchil macrosite, a group of small size separated from the rest is distinguished. We had detected this grouping in other sites studied by our team in La Rioja and in the work of drawing up scatter plots and frequency histograms of the measurements, on a global scale, of the length and width of the theropod footprints (these data are available on request from Farlow [farlow@pfw.edu]). We analyze the particular grouping of small footprints, providing the study of the theropod footprint measurements of 14 new sites with dinosaur footprints in Imilchil and the graphical representation (frequency histograms and scatter plots) of the global data. The work has been done by projecting and comparing the length and width measurements of the Imilchil footprints and the global ones of what we call circumscribed geographical environments (TGC or Temporal Geographical Circumscriptions [cf. Mínguez Ceniceros et al., 2022]). The ichnotaxonomic study of the footprints referred to in this work can be found in Masrour et al., (2023). Finally we interpret the position of maxima and minima in the frequency diagrams, both of Imilchil, and of the other areas in which Farlow has found records (up to the year 2022).

Child Health Dataset Publishing and Mining Based on Differential Privacy Preservation

With the emergence and development of application requirements such as data analysis and publishing, it is particularly important to use differential privacy protection technology to provide more reliable, secure, and compliant datasets for research in the field of children’s health. This paper focuses on the differential privacy protection of the ultrasound examination health dataset of adolescents in southern Texas from three aspects: differential privacy protection with output perturbation on basic statistics, publication of differential privacy marginal histogram and synthesized data, and a machine learning differential privacy learning algorithm. Firstly, differential privacy protection results with output perturbation show that Laplace and Gaussian mechanisms for numerical data, as well as the exponential mechanism for non-numerical data, can achieve the goal of protecting privacy. The exponential mechanism provides higher privacy protection. Secondly, a differential privacy marginal histogram with four attributes can be obtained with an appropriate privacy budget that approximates the marginal histogram of the original data. In order to publish synthetic data, we construct a synthetic query to obtain the corresponding differential privacy histogram for two attributes. Further, a synthetic dataset can be constructed by following the data distribution of the original dataset and the quality of the synthetic data publication can also be evaluated by the mean square error and error rate. Finally, consider a differential privacy logistic regression model under machine learning to predict whether children have fatty liver in binary classification tasks. The experimental results show that the model combined with quadratic perturbation has better accuracy and privacy protection. This paper can provide differential privacy protection models under different demands, which provides important data release and analysis options for data managers and research organizations, in addition to enriching the research on child health data releasing and mining.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients

PurposeThis study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).Materials and methodsThis study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.ResultsIn this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.ConclusionIntegrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Artificial Intelligence Assistive Tool for Radiotherapy Plan Evaluation Based on Analysis of Integral Dose

ABSTRACT Aim: The aim of the present study is to propose an in-house developed artificial intelligence (AI) tool based on Python programming for the purpose of integral doses (IDs) calculation useful in plan evaluation in modern radiotherapy techniques. Materials and Methods: Retrospectively, curative radiotherapy plans of cancer head and neck planned with intensity-modulated radiation therapy techniques employing seven and nine photon beams of 6 MV, were included in this study. The derived dose-volume histogram data were analyzed for the calculation of ID for each of the contoured structures including high-risk planning target volume (HR-PTV) and surrounding normal structures using an in-house developed Python program. Results: In this study, variation of ID between nine-beam and seven-beam plans was calculated. It was found that the ID for HR-PTV volume was almost equal in both nine and seven beam plans with the percentage variation range 0.4%–1.4%, however, significant variation up to 14.4% in the ID of organ at risk was found. Furthermore, we utilized the standard deviation (SD) as a metric to assess the variability of the ID within the PTV and the surrounding normal tissues. The HR-PTV exhibited a low SD of 0.71, suggesting consistent ID patterns. In contrast, the organs at risk (OAR) exhibited noteworthy variations in SD values, with some reaching as high as 16.75. The SD was relatively elevated in the OAR in comparison to the HR-PTV. These elevated SD values within the OAR indicate significant dose variability across different patients. Conclusion: It is found that ID increases as the number of beams increases. The Python program used in this study for the calculation of ID, as an AI assistive tool for plan evaluation, can be run on the TPS or on a side-by-side computer which may be helpful in finalizing radiotherapy plans.

Efficient computational method using random matrices describing critical thermodynamics

Our study emphasizes the efficacy of employing matrices resembling Wishart matrices, derived from magnetization time series data within specific dynamics, to elucidate phase transitions and critical phenomena in the Q-state Potts model. Through the application of appropriate statistical methods, we not only identify second-order transitions but also distinguish weaker first-order transitions by carefully analyzing the density of eigenvalues and their fluctuations. Additionally, we investigate the method’s sensitivity to stronger first-order transition points. Notably, we establish a robust correlation between the system’s actual thermodynamics and the spectral thermodynamics encapsulated within the eigenvalues. Our findings are further supported by correlation histograms of the time series data, revealing insightful patterns. Building upon our core results, we provide a didactic analysis that draws parallels between the spectral properties of criticality in a spin system and matrices intentionally imbued with correlations (a toy model). Within this framework, we observe a universal behavior characterized by the distribution of eigenvalues into two distinct groups, separated by a gap dependent on the level of correlation, influenced by temperature-induced changes in the spin system.

Dose to organs at risk for total body irradiation: Single-institution data using the modulated arc total body irradiation technique.

Organs at risk (OAR) dose reporting for total body irradiation (TBI) patients is limited, and standardly reported only as mean doses to the lungs and kidneys. Consequently, dose received and effects on other OAR remain unexplored. To remedy this gap, this study reports dose data on an extensive list of OAR for patients treated at a single institution using the modulated arc total body irradiation (MATBI) technique. An audit was undertaken of all patients treated with MATBI between January 2015 and March 2021 who had completed their course of treatment. OAR were contoured on MATBI patient treatment plans, with 12Gy in six fraction prescription. OAR dose statistics and dose volume histogram data are reported for the whole body, lungs, kidneys, bones, brain, lens, heart, liver and bowel bag. The OAR dose data for 29 patients are reported. Mean dose results are body 11.77Gy, lungs 9.86Gy, kidneys 11.84Gy, bones 12.03Gy, brain 12.12Gy, right lens 12.31Gy, left lens 12.64Gy, heart 11.07Gy, liver 11.81Gy and bowel bag 12.06Gy. Dose statistics at 1-Gy intervals of V6-V13 for lungs and V10-V13 for kidneys are also included. This is the first time an extensive list of OAR data has been reported for any TBI technique. Due to the paucity of reporting, this information could be used by centres implementing the MATBI technique, in addition to aiding comparison between TBI techniques, with the potential for greater understanding of the relationship between dose volume data and toxicity.

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Face Recognition Attendance System

Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

Dosimetric comparison of proton therapy and CyberKnife in stereotactic body radiation therapy for liver cancers

Stereotactic body radiation therapy (SBRT) has been increasingly used for the ablation of liver tumours. CyberKnife and proton beam therapy (PBT) are two advanced treatment technologies suitable to deliver SBRT with high dose conformity and steep dose gradients. However, there is very limited data comparing the dosimetric characteristics of CyberKnife to PBT for liver SBRT. PBT and CyberKnife plans were retrospectively generated using 4DCT datasets of ten patients who were previously treated for hepatocellular carcinoma (HCC, N = 5) and liver metastasis (N = 5). Dose volume histogram data was assessed and compared against selected criteria; given a dose prescription of 54 Gy in 3 fractions for liver metastases and 45 Gy in 3 fractions for HCC, with previously published consensus-based normal tissue dose constraints. Comparison of evaluation parameters showed a statistically significant difference for target volume coverage and liver, lungs and spinal cord (p < 0.05) dose, while chest wall and skin did not indicate a significant difference between the two modalities. A number of optimal normal tissue constraints was violated by both the CyberKnife and proton plans for the same patients due to proximity of tumour to chest wall. PBT resulted in greater organ sparing, the extent of which was mainly dependent on tumour location. Tumours located on the liver periphery experienced the largest increase in organ sparing. Organ sparing for CyberKnife was comparable with PBT for small target volumes.

The Effect of Hyperlipidemia on Cardiovascular Diseases: A Risk Factor in Cardiovascular Diseases at FMH Hospital Lahore

Background: Cardiovascular diseases (CVDs) represent a major global health concern, causing approximately 17.9 million deaths annually, accounting for 32% of all global deaths. Among these fatalities, 85% are attributed to heart attacks and strokes. Hyperlipidemia, a common metabolic disorder, significantly contributes to the risk of CVDs by causing disturbances in lipid metabolism, including elevated levels of low-density lipoprotein cholesterol (LDL-C), increased triglycerides, elevated total cholesterol (TC), and decreased levels of high-density lipoprotein cholesterol (HDL-C). Objective: To determine the impact of hyperlipidemia on cardiovascular diseases, focusing on its role as a prominent risk factor. Methods: This retrospective study was conducted at the Department of Pathology, Fatima Memorial Hospital, Lahore, from June 2023 to December 2023, following approval from the Institutional Review Board (IRB). Data were collected using a non-probability convenient sampling technique. The sample size was calculated based on a 95% confidence level, an estimated prevalence of hyperlipidemia in CVD patients of 4.5%, and a desired margin of error, resulting in a sample size of 65-70 patients. Inclusion criteria included CVD patients diagnosed with hyperlipidemia, while patients with hyperlipidemia but no CVD were excluded. Data were collected from medical records using a self-designed Performa. Statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp.). Categorical variables were expressed as frequencies and percentages, while continuous variables were presented as mean ± standard deviation (SD). Bar charts were used for categorical data and histograms for continuous data. A p-value of less than 0.05 was considered statistically significant. Chi-square analysis was used to identify correlations between lipid levels and CVD status. Results: Out of the 65 subjects, 45 (69.2%) had no CVD, while 20 (30.8%) were diagnosed with CVD. Total cholesterol levels were &lt;200 mg/dL in 36 patients (55.4%), 200-240 mg/dL in 15 patients (23.1%), and &gt;240 mg/dL in 14 patients (21.5%). LDL levels were &lt;100 mg/dL in 21 patients (32.3%), 130-189 mg/dL in 38 patients (58.5%), and &gt;190 mg/dL in 6 patients (9.2%). HDL levels were &lt;39 mg/dL in 36 patients (55.4%) and &gt;40 mg/dL in 29 patients (44.6%). Triglyceride levels were &lt;150 mg/dL in 15 patients (23.1%), 150-199 mg/dL in 30 patients (46.2%), and 200-499 mg/dL in 20 patients (30.8%). Chi-square analysis revealed a significant association between triglyceride levels and CVD status (p &lt; 0.001) and significant results for LDL and HDL levels (p &lt; 0.005). Conclusion: This study highlights the profound impact of hyperlipidemia on cardiovascular health, emphasizing its role as a significant risk factor for CVDs. The strong association between elevated lipid levels and increased risk of cardiovascular events underscores the need for effective lipid management strategies to reduce the burden of cardiovascular diseases and improve patient outcomes.