Learning Potential Research Articles

Conjunctivitis Detection: A Comprehensive Review of Deep Learning Approaches

Background: Conjunctivitis, often referred to as "pink eye" is a highly prevalent inflammation of the conjunctiva, a thin, transparent membrane lining the white part of your eye and the inner surface of your eyelids. This inflammation triggers a cascade of symptoms that can range from mild annoyance to significant discomfort, affecting people of all ages worldwide. The three main types of conjunctivitis are viral, bacterial, and allergy. Some common symptoms include redness of the white part of the eye, ranging from watery to thick and pus-like, discharge from the eye, itching or burning sensation, and a Gritty feeling in the eye. Methods: Several deep-learning techniques for conjunctivitis detection have been developed due to their simplicity of use and their affordability. This systematic review delves into the burgeoning field of machine learning within healthcare, specifically seeking viable approaches for detecting conjunctivitis. We embark on a comparative analysis of the most successful ML algorithms currently in use regarding machine learning, including evaluation metrics, image augmentation, and the origin and size of the dataset used. Results: The results of this study provide compelling evidence for the feasibility and potential benefits of using DL algorithms for conjunctivitis detection. Conclusion: This review sheds light on the potential of machine learning in detecting Conjunctivitis, providing scientific evidence for its feasibility. By analyzing images, diagnoses, and clinical data within the medical field, the review explores how machine and deep learning algorithms can offer a wide-ranging approach to conjunctivitis detection.

Analytical ab initio hessian from a deep learning potential for transition state optimization

Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

Thawed Gaussian Wavepacket Dynamics with Δ-Machine-Learned Potentials.

A method for performing variable-width (thawed) Gaussian wavepacket (GWP) variational dynamics on machine-learned potentials is presented. Instead of fitting the potential energy surface (PES), the anharmonic correction to the global harmonic approximation (GHA) is fitted using kernel ridge regression─this is a Δ-machine learning approach. The training set consists of energy differences between ab initio electronic energies and values given by the GHA. The learned potential is subsequently used to propagate a single thawed GWP by using the time-dependent variational principle to compute the autocorrelation function, which provides direct access to vibronic spectra via its Fourier transform. We applied the developed method to simulate the photoelectron spectrum of ammonia and found excellent agreement between theoretical and experimental spectra. We show that fitting the anharmonic corrections requires a smaller training set as compared to fitting total electronic energies. We also demonstrate that our approach allows to reduce the dimensionality of the nuclear space used to scan the PES when constructing the training set. Thus, only the degrees of freedom associated with large-amplitude motions need to be treated with Δ-machine learning, which paves a way for reliable simulations of vibronic spectra of large floppy molecules.

Reinforcement learning-based assimilation of the WOFOST crop model

Crop model assimilation is a crucial technique for improving the accuracy and precision of crop models by integrating observational data and crop models. Although conventional data assimilation methods such as Kalman filtering and variational methods have been widely applied, these methods often face limitations in data quality, model bias, and high computational complexity. This study explored the potential of reinforcement learning (RL) for crop model assimilation, which has the advantage of not requiring large datasets. Based on the WOFOST crop model, two RL environments were constructed: a Daily-Data Driven approach and a Time-Series Driven approach. The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. The assimilation results were compared with the commonly used SUBPLEX optimization algorithm using four-year field measurement data and a public dataset with added random errors. Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. In a multi-year stability test, the Time-Series Driven RL model and SUBPLEX had similar assimilation performance. This study demonstrates the potential of RL for crop model assimilation, providing a novel approach to overcome the limitations of conventional assimilation algorithms. The findings suggest that RL-based crop model assimilation can improve model accuracy and efficiency, with potential for practical applications in precision agriculture.

Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques. The research approached five prominent machine learning algorithms, including Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and Regression Trees (RPART), and AdaBoost, in the context of IVF success prediction. The study also incorporated GA as a feature selection method to enhance the predictive models' robustness. Findings demonstrate that AdaBoost, particularly when combined with GA feature selection, achieved the highest accuracy rate of 89.8%. Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. Ten crucial features, including female age, AMH, endometrial thickness, sperm count, and various indicators of oocyte and embryo quality, were identified as key determinants of IVF success. These findings underscore the potential of machine learning and feature selection techniques to assist IVF clinicians in providing more accurate predictions, enabling tailored treatment plans for each patient. Future research and validation can further enhance the practicality and reliability of these predictive models in clinical IVF practice.

AI driven interpretable deep learning based fetal health classification

In this study, a deep learning model is proposed for the classification of fetal health into 3 categories: Normal, suspect, and pathological. The primary objective is to utilize the power of deep learning to improve the efficiency and effectiveness of diagnostic processes. A deep neural network (DNN) model is proposed for fetal health analysis using data obtained from Cardiotocography (CTG). A dataset containing 21 attributes is used to carry out this work. The model incorporates multiple hidden layers, augmented with batch normalization and dropout layers for improved generalization. This study assesses the model's interpretation ability in fetal health classification using explainable deep learning. This enhances transparency in decision-making of the classifier model by leveraging feature importance and feature saliency analysis, fostering trust and facilitating the clinical adoption of fetal health assessments. Our proposed model demonstrates a remarkable performance with 0.99 accuracy, 0.93 sensitivity, 0.93 specificity, 0.96 AUC, 0.93 precision, and 0.93 F1 scores in classifying fetal health. We also performed comparative analysis with six other models including Logistic Regression, KNN, SVM, Naive Bayes, Random Forest, and Gradient Boosting to assess and compare the effectiveness of our model and the accuracies of 0.89, 0.88, 0.90, 081, 0.93, and 0.93 were achieved respectively by these baseline models. The results revealed that our proposed model outperformed all the baseline models in terms of accuracy. This indicates the potential of deep learning in improving fetal health assessment and contributing to the field of obstetrics by providing a robust tool for early risk detection.

Deep learning-driven channel coding: enhancing performance in LDS-CDMA and NOMA systems

This paper presents an innovative approach to channel coding for LDS-CDMA and NOMA systems through the integration of deep learning techniques. By leveraging neural networks within the channel coding process, the proposed model achieves substantial improvements in error correction, leading to a significant reduction in Bit Error Rate (BER) and computational complexity when compared to traditional methods such as Unpunctured Turbo Trellis Coded Modulation (UTTCM). The results demonstrate that the deep learning-based system is particularly effective in challenging communication environments, such as those with low Signal-to-Noise Ratios (SNR) and high levels of multi-user interference, where it outperforms conventional coding techniques. This research underscores the potential of deep learning to enhance the efficiency and adaptability of wireless communication systems, particularly as networks evolve towards 6G and beyond. The flexibility offered by this approach allows for improved handling of dynamic and complex environments, ensuring higher data throughput and reduced latency. Future work may focus on implementing the proposed model in real-time applications, as well as investigating its synergy with emerging technologies like massive MIMO and NOMA, which could lead to even greater performance enhancements in next-generation communication systems.

Constructivist Approach to Language Learning: Linking Piaget's Theory to Modern Educational Practice

This study aims to explore the application of the constructivism approach in language learning by connecting Jean Piaget's theory of cognitive development with modern educational practices. Constructivism theory emphasizes that learning is an active process in which students build knowledge through experience and interaction with the environment. In the context of language learning, this approach is considered relevant to increase student engagement and support more meaningful learning. This study uses a qualitative approach with a case study design, involving in-depth interviews, classroom observations, and document analysis. Participants consisted of 10 teachers and 20 students who were selected by purposive sampling based on their experience in applying constructivist learning methods. The results show that the constructivism approach can increase students' active participation, improve critical thinking skills, and provide a more contextual and relevant language learning experience. Teachers who apply Piaget's principles, such as experiential learning, reflection, and collaboration, report increased student learning motivation. However, challenges in implementing this method include time constraints, a rigorous curriculum, and a lack of support resources for project-based learning. The study concludes that although constructivist approaches have great potential in language learning, further support is needed in terms of curriculum flexibility and resources. Recommendations for educators include increased use of technology and project-based teaching strategies to support more interactive and participatory language learning.

Thermal transport in C6N7 monolayer: a machine learning based molecular dynamics study

The successful synthesis of a novel C6N7carbon nitride monolayer offers expansive prospects for applications in the fields of semiconductors, sensors, and gas separation technologies, in which the thermal transport properties of C6N7are crucial for optimizing the functionality and reliability of these applications. In this work, based on our developed machine learning potential (MLP), molecular dynamics (MD) simulations including homogeneous non-equilibrium, non-equilibrium, and their respective spectral decomposition methods are performed to investigate the effects of phonon transport, temperature, and length on the thermal conductivity of C6N7monolayer. Our results reveal that low-frequency and in-plane phonon modes dominate the thermal conductivity. Notably, thermal conductivity decreases with an increase in temperature due to temperature-induced increase in phonon-phonon scattering of in-plane phonon modes, while it increases with an extension in sample length. Our findings based on MD simulations with MLP contribute new insights into the lattice thermal conductivity of holey carbon nitride compounds, which is helpful for the development of next-generation electronic and photonic devices.

State of the Art and Potentialities of Graph-level Learning

Graphs have a superior ability to represent relational data, such as chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks, including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures. While these methods benefit from good interpretability, they often suffer from computational bottlenecks, as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, no comprehensive survey reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article fills this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. In addition, the evolution and interaction between methods from these four branches within their developments are examined to provide an in-depth analysis. This is followed by a brief review of the benchmark datasets, evaluation metrics, and common downstream applications. Finally, the survey concludes with an in-depth discussion of 12 current and future directions in this booming field.

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

BackgroundForecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.MethodsThe dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting.ResultsWe found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data.ConclusionsActive SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the national COVID cohort collaborative (N3C)

Abstract Objectives We propose and validate a domain knowledge-driven classification model for diagnosing Post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHR) data. Materials and Methods We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating Characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data. Results Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545. Discussion The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model's architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model’s generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge. Conclusion The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long COVID symptoms.

A Systematic Review of Flipped Learning in EFL Education

Research on flipped learning in English as a Foreign Language (EFL) education has garnered substantial scholarly attention in recent years. This systematic review aims to elucidate the potential of flipped learning within the framework of foreign language education. A total of thirty articles from Social Science Citation Index (SSCI) journals, published between 2018 and 2022, were included in this review based on predetermined inclusion and exclusion criteria. The analysis focused on examining the research contexts, methodologies and foci, learning management systems, learning theories, and learning activities. The findings indicate that the majority of studies predominantly involved higher education students, with a significant number employing a quasi-experimental research design. Furthermore, the flipped learning process was largely informed by constructivist learning theory. The results suggest that the primary advantage of flipped learning lies in its capacity to enhance students' writing, speaking, and overall academic performance. Additionally, the review underscores that the integration of emerging technologies, such as augmented reality, automatic recognition, and artificial intelligence chatbots, into learning activities serves as an effective strategy for improving students' language skills and engagement.

Special applets for elucidating the difference between prediction intervals and confidence intervals—Why are arising ovals actually ellipses?

AbstractIn the last years special “ovals” appear increasingly often in diagrams and applets for discussing crucial items of statistical inference (when dealing with confidence intervals for an unknown probability p; approximation of the binomial distribution by the normal distribution; especially in German literature, see e.g. [Meyer, Stochastik in der Schule 2011; 31:18–22] or [Riemer, Statistik unterrichten. Eine handlungsorientierte Didaktik der Stochastik; 2023, p. 69ff]). These applets concerning confidence intervals aim at illustrating vividly the crucial differences (as we know, often vague for students) and connections between confidence intervals and prediction intervals. In English literature such applets seem to be not so well known, thus at the beginning, we describe how they work and highlight their high potential for teaching and learning. The fact that the mentioned ovals are, actually, ellipses is not important for stochastic purposes which is probably the reason why one can hardly find related explanations in the literature. Another aim of this paper is to explain the ellipse shape in a preferably elementary manner involving geometry, even spatial geometry.

Involvement in serious incident investigations: a qualitative documentary analysis of NHS trust policies in England

BackgroundThe considered shift from individual blame and sanctions towards a commitment to system-wide learning from incidents in healthcare has led to increased understanding of both the moral and epistemic importance of involving those affected. It is important to understand whether and how local policy describes and prompts involvement with a view to understanding the policy landscape for serious incident investigations in healthcare.This study aimed to explore the way in which involvement of those affected by serious incidents is represented in incident investigation policy documents across acute and mental health services in the English NHS, and to identify guidance for more effective construction of policy for meaningful involvement.MethodsWe conducted a documentary analysis of 43 local serious incident investigation policies to explore the way in which involvement in serious incident investigations is represented in policy documents across acute and mental health services in the NHS in England.ResultsThree headline findings were generated. First, we identified involvement as a concept was conspicuous by its absence in policy documents. Direct reference to support or involvement of those affected by serious incidents was lacking. Even where involvement and support were recognised as important, this was described as a passive process rather than there being moral or epistemic justification for more active contribution to learning. Second, learning from serious incidents was typically described as a high priority but the language used was unclear and ‘learning’ was more often positioned as construction of an arbitrary set of recommendations rather than a participatory process of deconstruction and reconstruction of specific systems and processes. Third, there was an emphasis placed on a just and open culture but paradoxically this was reinforced by expected compliance, positioning investigations as a tool through which action is governed rather than an opportunity to learn from and with the experiences and expertise of those affected.ConclusionsMore effective representation in policy of the moral and epistemic reasons for stakeholder involvement in serious incident investigations may lead to better understanding of its importance, thus increasing potential for organisational learning and reducing the potential for compounded harm. Moreover, understanding how structural elements of policy documents were central to the way in which the document is framed and received is significant for both local and national policy makers to enable more effective construction of healthcare policy documents to prompt meaningful action.

Coupling Taguchi experimental designs with deep adaptive learning enhanced AI process models for experimental cost savings in manufacturing process development

The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study the effect of these parameters on workpiece surface roughness and tool life. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges are identified for output parameters. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.

Performance Evaluation of Deep Learning Architectures for Blood Pressure Estimation Using Photoplethysmography

High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.

Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.