Advanced Machine Learning Algorithms Research Articles

Harnessing Artificial Intelligence in Healthcare Analytics: From Diagnosis to Treatment Optimization

The use of artificial intelligence (AI) in healthcare analytics has brought about a transformation in the medical field of diagnosis and treatment optimization. AI technologies have unmatched abilities in processing substantial amounts of medical data and extracting valuable insights by combining big data analytics and advanced machine learning algorithms. AI algorithms provide healthcare professionals with enhanced accuracy and speed in diagnosing illnesses, predicting patient results, and tailoring treatment plans across the entire healthcare journey. This abstract explores how artificial intelligence (AI) can revolutionize healthcare analytics in various areas such as genomics, electronic health records (EHRs), medical imaging, and clinical decision support systems. Healthcare providers can improve healthcare services by optimizing workflows, enhancing patient outcomes, and using AI-driven initiatives to make services more accessible and high in quality. In order to ensure ethical and accountable use of AI in healthcare, it is necessary to address problems such as algorithm bias, data privacy worries, and regulatory obstacles. Despite these challenges, the ongoing advancement of AI technologies has vast potential to transform patient care models and healthcare delivery methods.

Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron-Deficiency Anemia Using Machine Learning Algorithms.

In response to the nuanced glycemic challenges faced by women with iron deficiency anemia (IDA) associated with diabetes, this study uses advanced machine learning algorithms to redefine hemoglobin (Hb)A1c measurement values. We aimed to improve the accuracy of glycemic interpretation by recognizing the critical interaction between erythrocytes, iron, and glycemic levels in this specific demographic group. This retrospective observational study included 17,526 adult women with HbA1c levels recorded from 2017 to 2022. Samples were classified as diabetic, prediabetic, or non-diabetic based on HbA1c and fasting blood glucose (FBG) levels for distribution analysis without impacting model training. Support Vector Machines, Linear Regression, Random Forest, and K-Nearest Neighbor algorithms as machine learning (ML) methods were used to predict HbA1c levels. Following the training of the model, HbA1c values were predicted for the IDA samples using the trained model. According to our results, there has been a 0.1 unit change in HbA1c values, which has resulted in a clinical decision change in some patients. Using ML to analyze HbA1c results in women with IDA may unveil distinctions among patients whose HbA1c values hover near critical medical decision thresholds. This intersection of technology and laboratory science holds promise for enhancing precision in medical decision-making processes.

Numerical Data Based Fertilizer Recommendation System for Farmer

In modern agriculture, optimizing fertilizer use is crucial for maximizing crop yields, maintaining soil health, and reducing environmental impact. The Numerical Data-Based Fertilizer Recommendation System (NDFRS) for farmers addresses these needs by leveraging data-driven approaches to provide precise fertilizer recommendations tailored to specific crop and soil conditions. This system integrates soil nutrient analysis, crop nutrient requirements, and environmental factors to generate customized fertilizer plans. The NDFRS employs advanced machine learning algorithms to analyze extensive datasets, including soil test results, historical crop yield data, weather patterns, and geographical information. By incorporating these variables, the system can predict the optimal type and amount of fertilizers required for different crops at various growth stages. This ensures that nutrients are supplied efficiently, enhancing crop productivity while minimizing excess fertilizer use and associated environmental risks. The user- friendly interface of the NDFRS allows farmers to input relevant data easily and receive actionable recommendations. Additionally, the system offers real-time updates and adaptive recommendations based on changing environmental conditions and crop growth stages. Field trials and validation studies demonstrate the system's efficacy in improving crop yields and promoting sustainable farming practices. Keywords: Tensor Flow, Deep Learning.

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions

In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.

Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.

Comparative Analysis Highlights Uniconazole's Efficacy in Enhancing the Cold Stress Tolerance of Mung Beans by Targeting Photosynthetic Pathways.

Soybean (Glycine max) and mung bean (Vigna radiata) are key legumes with global importance, but their mechanisms for coping with cold stress-a major challenge in agriculture-have not been thoroughly investigated, especially in a comparative study. This research aimed to fill this gap by examining how these two major legumes respond differently to cold stress and exploring the role of uniconazole, a potential stress mitigator. Our comprehensive approach involved transcriptomic and metabolomic analyses, revealing distinct responses between soybean and mung bean under cold stress conditions. Notably, uniconazole was found to significantly enhance cold tolerance in mung bean by upregulating genes associated with photosynthesis, while its impact on soybean was either negligible or adverse. To further understand the molecular interactions, we utilized advanced machine learning algorithms for protein structure prediction, focusing on photosynthetic pathways. This enabled us to identify LOC106780309 as a direct binding target for uniconazole, confirmed through isothermal titration calorimetry. This research establishes a new comparative approach to explore how soybean and mung bean adapt to cold stress, offers key insights to improve the hardiness of legumes against environmental challenges, and contributes to sustainable agricultural practices and food security.

Enhancing Immunotherapy Response Prediction in Metastatic Lung Adenocarcinoma: Leveraging Shallow and Deep Learning with CT-Based Radiomics across Single and Multiple Tumor Sites.

This study aimed to evaluate the potential of pre-treatment CT-based radiomics features (RFs) derived from single and multiple tumor sites, and state-of-the-art machine-learning survival algorithms, in predicting progression-free survival (PFS) for patients with metastatic lung adenocarcinoma (MLUAD) receiving first-line treatment including immune checkpoint inhibitors (CPIs). To do so, all adults with newly diagnosed MLUAD, pre-treatment contrast-enhanced CT scan, and performance status ≤ 2 who were treated at our cancer center with first-line CPI between November 2016 and November 2022 were included. RFs were extracted from all measurable lesions with a volume ≥ 1 cm3 on the CT scan. To capture intra- and inter-tumor heterogeneity, RFs from the largest tumor of each patient, as well as lowest, highest, and average RF values over all lesions per patient were collected. Intra-patient inter-tumor heterogeneity metrics were calculated to measure the similarity between each patient lesions. After filtering predictors with univariable Cox p < 0.100 and analyzing their correlations, five survival machine-learning algorithms (stepwise Cox regression [SCR], LASSO Cox regression, random survival forests, gradient boosted machine [GBM], and deep learning [Deepsurv]) were trained in 100-times repeated 5-fold cross-validation (rCV) to predict PFS on three inputs: (i) clinicopathological variables, (ii) all radiomics-based and clinicopathological (full input), and (iii) uncorrelated radiomics-based and clinicopathological variables (uncorrelated input). The Models' performances were evaluated using the concordance index (c-index). Overall, 140 patients were included (median age: 62.5 years, 36.4% women). In rCV, the highest c-index was reached with Deepsurv (c-index = 0.631, 95%CI = 0.625-0.647), followed by GBM (c-index = 0.603, 95%CI = 0.557-0.646), significantly outperforming standard SCR whatever its input (c-index range: 0.560-0.570, all p < 0.0001). Thus, single- and multi-site pre-treatment radiomics data provide valuable prognostic information for predicting PFS in MLUAD patients undergoing first-line CPI treatment when analyzed with advanced machine-learning survival algorithms.

The Transformative Role of Microsoft Azure AI in Healthcare

This paper explores the transformative impact of Azure AI technologies on healthcare, focusing on diagnostics, predictive analytics, operational efficiency, and drug discovery. Azure AI tools are reshaping healthcare delivery by harnessing data-driven insights and advanced machine learning algorithms to enhance patient outcomes significantly. Azure AI enables more precise diagnostics through advanced image recognition and pattern analysis, improving the speed and accuracy of medical assessments. It supports predictive analytics models that personalize treatment plans based on individual patient data, thereby optimizing care and reducing risks. In hospital settings, Azure AI enhances operational efficiencies by predicting maintenance needs and optimizing resource allocation, streamlining workflows and improving overall service delivery. Furthermore, Azure AI accelerates drug discovery processes by analyzing extensive datasets to identify potential drug candidates swiftly. These advancements not only improve the efficiency and effectiveness of healthcare services but also hold promise for addressing healthcare disparities and promoting equitable access to quality care globally.

In-depth insight into the driving factors of the compressive strength development of MKPC based on interpretable machine learning methods

Magnesium potassium phosphate cement (MKPC) is a kind of Mg-chemically bonded phosphate ceramic commonly used for rapidly repairing dilapidated structures. In this study, a compressive strength dataset of MKPC was constructed, and four advanced machine learning (ML) algorithms (XGBoost, RF, GBDT and ANN) were selected to establish a high-precision compressive strength prediction model of MKPC. The SHAP and PDP methods are also used for interpretability analysis of ML-MKPC models. The XGBoost model has good generalizability and reliability while achieving high prediction accuracy. The RF and GBDT models performed similarly to the XGBoost model on the training set but performed poorly on the testing set. The ANN model is poorly trained on both the training and testing sets, with a risk of underfitting. The R2 of the XGBoost model at the different compressive strength stages still reaches above 0.80, indicating that it not only captures the complex relationships of the overall dataset well but also effectively predicts the staged strength dataset. Feature importance analysis revealed that the curing age (T), water-to-binder ratio (W/B), mineral admixtures-to-binder ratio (MA/B) and phosphate-to-magnesium ratio (P/M) are the principal variables affecting the compressive strength of MKPC. The partial interpretation shows that the optimum value range is determined when W/B is 0.10–0.18, MA/B is 0–0.20, P/M is 0.40–1.0, and R/M is 0–0.12. The composition of mineral admixtures with high-Ca, high-Si and low-Al systems seems to be more conducive to participating in the hydration reaction of MKPC. The ML-MKPC compressive strength prediction model developed in this study can provide theoretical support for the subsequent composition design and performance optimization of MKPC.

Firm-level climate change risk and adoption of ESG practices: a machine learning prediction

PurposeBy combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.Design/methodology/approachWe used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.FindingsOur findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.Practical implicationsThe insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).Originality/valueTo the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.

Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images

Most studies have failed to focus on geriatric diseases in the present era of quick advancement in medical science. Diseases like Parkinson’s display their symptoms at a later stage and make a complete recovery almost doubtful. Parkinson’s disease is a neurodegenerative disorder that affects movement and motor control systems. It is named after Dr. James Parkinson, the first person affected by this disease. Parkinson’s slowly worsens over time, leading to a variety of syndromes that can impact a person’s daily life activities. More than 95% of Parkinson’s Disease (PD) patients stated that they have exhibited voice impairment and micrographic disability. This model takes advantage of both advanced machine learning algorithms and modern image processing techniques, resulting in effectiveand efficient predictionPD. To further enhance the accuracy of the model, we have incorporated additional algorithms such as Random Forest and K-nearest Neighbour. Random forest classifier has a detection accuracy of 92%and sensitivity of 0.95%. The performance has been assessed with a reliable dataset from the University of California Irvine Machine Learning repository for voice parameters and a dataset from Kaggle for Handwriting images which includes wavy images and spiral images. Our proposed model has achieved the highest accuracy of 95% which outperformed the previous model or experiment on the same dataset.

Optimal signal quality index for remote photoplethysmogram sensing

Remote photoplethysmography (rPPG) enables non-invasive monitoring of circulatory signals using mobile devices, a crucial advancement in biosensing. Despite its potential, ensuring signal quality amidst noise and artifacts remains a significant challenge, particularly in healthcare applications. Addressing this, our study focuses on a singular signal quality index (SQI) for rPPG, aimed at simplifying high-quality video capture for heart rate detection and cardiac assessment. We introduce a practical threshold for this SQI, specifically the signal-to-noise ratio index (NSQI), optimized for straightforward implementation on portable devices for real-time video analysis. Employing (NSQI < 0.293) as our threshold, our methodology successfully identifies high-quality cardiac information in video frames, effectively mitigating the influence of noise and artifacts. Validated on publicly available datasets with advanced machine learning algorithms and leave-one-out cross-validation, our approach significantly reduces computational complexity. This innovation not only enhances efficiency in health monitoring applications but also offers a pragmatic solution for remote biosensing. Our findings constitute a notable advancement in rPPG signal quality assessment, marking a critical step forward in the development of remote cardiac monitoring technologies with extensive healthcare implications.

Utilizing Machine Learning for air pollution prediction, comprehensive impact assessment, and effective solutions in Kolkata, India

Escalating air pollution in urban areas is a matter of concern, and deteriorating air quality is having numerous impacts on human health and the environment. Kolkata is one of the most densely populated and highly polluted cities in India. The aim of this work is to predict the concentration of ambient PM2.5 using different air pollutants and meteorological parameters as predictor variables by using statistical and different Machine Learning techniques as well as to understand the influence of other air pollutants and meteorological factors in ambient PM2.5 prediction. Different advanced machine learning algorithms like Random Forest Regression, decision trees, k-nearest Neighbour, Support Vector Regression, Ridge Regression, Lasso Regression, and XGBoost have been used, and the results show that the XGBoost model exhibits higher linearity between predictions and observations, among other models. Moreover seasonal variation of the most influential factor for prediction of PM2.5 is also noticed during the analysis. This work adds to the broader comprehension of the convergence of environmental science, public health, and machine learning and it offers significant perspectives for sustainable urban planning and pollution control tactics in rapidly expanding metropolitan areas such as Kolkata.

Hybrid Time Series Model for Advanced Predictive Analysis in COVID-19 Vaccination

This study aims to enhance the prediction of COVID-19 vaccination trends using a novel integrated forecasting model, facilitating better public health decision-making and resource allocation during the pandemic. As the COVID-19 pandemic continues to impact global health, accurately forecasting vaccination trends is critical for effective public health response and strategy development. Traditional forecasting models often fail to capture the complex dynamics of pandemic-driven vaccination rates. The analysis utilizes a comprehensive dataset comprising over 68,487 entries, detailing daily vaccination statistics across various demographics and geographic locations. This dataset provides a robust foundation for modeling and forecasting efforts. It utilizes advanced time series analysis techniques and machine learning algorithms to accurately predict future vaccination patterns based on the Hybrid Harvest model, which combines the strengths of ARIMA and Prophet models. Hybrid Harvest exhibits superior performance, with mean-square errors (MSEs) of 0.1323, and root-mean-square errors (RMSEs) of 0.0305. Based on these results, the model is significantly more accurate than traditional forecasting methods when predicting vaccination trends. It offers significant advances in forecasting COVID-19 vaccination trends through integration of ARIMA and Prophet models. The model serves as a powerful tool for policymakers to plan vaccination campaigns efficiently and effectively.

PREDICTING STOCK MARKET TRENDS WITH PYTHON

Artificial Intelligence (AI) and machine learning (ML) have revolutionized the realm of stock market prediction, offering sophisticated tools to analyze vast volumes of data and anticipate market trends. This article provides a comprehensive overview of AI techniques, focusing on Python as the preferred platform for implementation. Beginning with an exploration of AI fundamentals, including machine learning and deep learning, it delves into various techniques employed for stock market prediction. Traditional statistical models such as linear regression and ARIMA are under scientific discussion alongside advanced ML algorithms like random forests and support vector machines. Moreover, the article highlights the efficacy of deep learning methodologies, particularly recurrent neural networks (RNNs) and long &amp; short-term memory (LSTM) networks, in capturing temporal dependencies within stock market data. We also explored innovative developments such as Generative Adversarial Networks (GANs) for their potential in revealing hidden patterns influencing price movements. Throughout the discussion, we concluded that Python emerges as the preferred programming language due to its simplicity, extensive libraries, and versatility. Key libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow play a pivotal role in data manipulation, preprocessing, and model development. The article outlines a structured approach to building predictive models, encompassing data collection, preprocessing, feature engineering, model selection, training, evaluation, and prediction. Despite the advancements in AI, challenges persist in stock market prediction, including market volatility, data quality issues, complexity of influencing factors, and risks of overfitting. Ultimately, we may witness AI and Python synergy, which empowers analysts and investors with deeper insights, enabling informed decision-making amidst the complexities of financial markets.

A trust distributed learning (D‐NWDAF) against poisoning and byzantine attacks in B5G networks

AbstractBeyond 5G networks, (B5G) introduce the emergence of a smart service and an automate management archetype of running network slices, such as the network data analytics function (NWDAF). These new architectures provide a large usage of advanced machine learning algorithms, in order to dynamically build efficient decisions. In this perspective, the Distributed Learning (D‐), known as A Distributed NWDAF Architecture (D‐NWDAF) has proved its efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Notwithstanding, FL is vulnerable to different attacks, where FL nodes (Leaf NWDAF: AI‐VNFs*) may upload malicious updates to the FL central (root NWDAF) entity so that it can cause a construction failure of the FL global model and affect the global performance. Moreover, attacks detection of FL‐based solutions give “black‐box” decisions about the performance of running network slices and the attacks detected. In other words, detection solutions do not provide any details about why and how such ML attacks decisions were made. Thus, such decisions cannot be properly trusted and comprehended by B5G slice managers. To resolve this issue, we leverage Dimensional Reduction (DR) and Explainable Artificial Intelligence (XAI) algorithms which aim to detect attacks and improve the transparency of black‐box FL attacks detection‐making process. In the present article, we design a novel DR‐XAI‐powered framework to detect attacks and explain the FL‐based attacks detection. We first build a deep learning model in a federated way, to predict key performance indicators (KPI) of network slices. Then, we try to detect the malicious FL nodes, using DR and several XAI models, such as RuleFit, in order to enhance the level of trustiness, transparency, and the explanation of the FL‐based attacks detection, while adhering the data privacy, to different B5G network stakeholders.

Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception

With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.

Machine Learning Techniques for Predicting Conductive Properties of New Materials

The study "Machine Learning Techniques for Predicting Conductive Properties of New Materials" explores the application of advanced machine learning (ML) algorithms to predict the conductive properties of novel materials, accelerating the discovery and development process in materials science. Traditional methods for assessing conductive properties are often time-consuming and expensive, necessitating a more efficient approach. This research leverages various ML techniques, including supervised learning algorithms such as support vector machines, decision trees, and neural networks, to analyze large datasets of material properties and predict conductivity with high accuracy. Feature selection and engineering processes are employed to identify the most significant attributes influencing conductivity. The study also compares the performance of different ML models, optimizing hyperparameters to enhance prediction reliability. Results demonstrate that ML models can significantly reduce the experimental burden, offering rapid and precise predictions that align closely with empirical data. The integration of ML in materials science presents a transformative approach, enabling faster identification of promising conductive materials, thereby fostering advancements in electronics, energy storage, and other technological domains. The study highlights the potential of ML to revolutionize material property prediction, paving the way for accelerated innovation and application in various industries.

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

TO ANALYZE THE LUNGS X-RAY IMAGES USING MACHINE LEARNING ALGORITHM: AN IMPLEMENTATION TO PNEUMONIA DIAGNOSIS

Introduction: Respiratory diseases, particularly pneumonia, pose a significant threat to human life. Pneumonia affects the respiratory function in the human body and is a dangerous lung disease. This study aims to propose a model for detecting pneumonia in chest XR images. By utilizing statistical-based features, relevant and informative features are extracted from lung X-ray images. Objective: The objective is to obtain high accuracy in pneumonia identification; the target of this work is to generate a model that can precisely recognize the presence of pneumonia by evaluating chest X-ray pictures. Method: The Method follows a three-phase approach: preprocessing, categorization, and extraction of features. Preprocessing is the stage when various filters are applied to the chest X-ray images to enhance their eminence and eradicate noise. The feature extraction phase involves extracting statistical-based features from the preprocessed images. These features capture relevant information regarding a pneumonia diagnosis. Finally, in the classification phase, algorithms for machine learning are employed to use the retrieved features to categorize the X-ray pictures as infected or uninfected. Result: The proposed model successfully detects the presence of pneumonia accurately. By leveraging advanced machine learning algorithms, the model achieves accurate X-ray image classification for the chest. Conclusion: This study concludes by presenting a model for detecting pneumonia by examining chest X-ray pictures. To accurately classify infected and non-infected lungs, the proposed model makes use of image dispensation methods and machine learning algorithms. The model's high accuracy in pneumonia detection can significantly contribute to early diagnosis and treatment.