Unsupervised Machine Learning Algorithms Research Articles

Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study.

Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy. Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach. In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

An Unsupervised Machine Learning Algorithm for Attack and Anomaly Detection in IoT Sensors

An Unsupervised Machine Learning Algorithm for Attack and Anomaly Detection in IoT Sensors

Unsupervised analysis of whole transcriptome data from human pluripotent stem cells cardiac differentiation

The main objective of the present work was to highlight differences and similarities in gene expression patterns between different pluripotent stem cell cardiac differentiation protocols, using a workflow based on unsupervised machine learning algorithms to analyse the transcriptome of cells cultured as a 2D monolayer or as 3D aggregates. This unsupervised approach effectively allowed to portray the transcriptomic changes that occurred throughout the differentiation processes, with a visual representation of the entire transcriptome. The results allowed to corroborate previously reported data and also to unveil new gene expression patterns. In particular, it was possible to identify a correlation between low cardiomyocyte differentiation efficiencies and the early expression of a set of non-mesodermal genes, which can be further explored as predictive markers of differentiation efficiency. The workflow here developed can also be applied to analyse other stem cell differentiation transcriptomic datasets, envisaging future clinical implementation of cellular therapies.

Technological barriers to creating regional resilience in digital platform‐based firms: Compound of performance sensitivity analysis and BIRCH algorithm

AbstractEntrepreneurial ventures face various problems contributing to the regional resilience of their districts. Among such firms are digital platform‐based businesses that could have exponential impacts—indeed, if they succeed in overcoming the barriers. Thus, this study aims to identify the major technological barriers to creating regional resilience in Iran's innovation districts. This study uses the analytical hierarchy process method and an unsupervised machine learning algorithm, as well as the Delphi technique. Online panel surveys are conducted to collect data from experts in the field of online social platforms. The findings show that digital literacy barriers and cultural barriers are the criteria with the uppermost and lowermost importance. Performance sensitivity analysis illustrates that considering the C1 and C4 criteria, the Digikala platform is more resistant to technological barriers compared to other platforms. Furthermore, based on C2 and C3 criteria, it can be stated that the Snapp platform reveals better resistance than the other platforms. The importance‐performance matrix also shows that all platforms need more attention in terms of performance. Besides, the results of the BIRCH analysis show that the present model's accuracy is 88%. Meanwhile, outputs show that experts are divided into four groups or four different thoughts.

Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis.

This study used an unsupervised machine learning algorithm, sidClustering and random forests, to identify clusters of risk behaviors of Bacterial Vaginosis (BV), the most common cause of abnormal vaginal discharge linked to STI and HIV acquisition. METHODS: Participants were 391 cisgender women in Miami, Florida, with a mean of 30.8 (SD = 7.81) years of age; 41.7% identified as Hispanic; 41.7% as Black and 44.8% as White. Participants completed measures of demographics, risk behaviors [sexual, medical, and reproductive history, substance use, and intravaginal practices (IVP)], and underwent collection of vaginal samples; 135 behavioral variables were analyzed. BV was diagnosed using Nugent criteria. We identified four clusters, and variables were ranked by importance in distinguishing clusters: Cluster 1: nulliparous women who engaged in IVPs to clean themselves and please sexual partners, and used substances frequently [n = 118 (30.2%)]; Cluster 2: primiparous women who engaged in IVPs using vaginal douches to clean themselves (n = 112 (28.6%)]; Cluster 3: primiparous women who did not use IVPs or substances [n = 87 (22.3%)]; and Cluster 4: nulliparous women who did not use IVPs but used substances [n = 74 (18.9%)]. Clusters were related to BV (p < 0.001). Cluster 2, the cluster of women who used vaginal douches as IVPs, had the highest prevalence of BV (52.7%). Machine learning methods may be particularly useful in identifying specific clusters of high-risk behaviors, in developing interventions intended to reduce BV and IVP, and ultimately in reducing the risk of HIV infection among women.

A vibration-based Machine Learning type Structural Health Monitoring methodology for populations of composite aerostructures under uncertainty

A robust to uncertainty Machine Learning (ML) based Structural Health Monitoring methodology for populations of composite aerostructures is postulated. The methodology is founded upon a number of unsupervised ML algorithms for damage detection and a supervised counterpart for damage characterization. Damage detection is specifically based on two types of Healthy Subspace representations: A Multiple Model (MM) and a varying radii Hyper-Sphere (HS) type. Both are built upon response-only vibration acceleration and/or strain signals at properly selected sensor locations. Based on them, Multiple Input Single Output (MISO) Transmittance Function AutoRegressive with eXogenous (TF-ARX) excitation data driven models representing the partial structural dynamics are obtained. Decision making is then based on the model parameter vector that may be transformed and reduced via Principal Component Analysis (PCA). Damage detection is achieved via multi-level information fusion using acceleration and/or strain sensors. Damage characterization, referring to damage type, location, and level determination, is achieved via a hierarchical cosine similarity based algorithm. The methodology is successfully assessed via hundreds of experiments using a population of small-scale composite coupons for the detection and characterization of Delamination and Impact damage under material/manufacturing, temperature, excitation, and experimental uncertainty.

Machine learning based classification of vector field configurations

Magnetic materials at the nanoscale are important for science and technology. A key aspect for their research and advancement is the understanding of the emerging magnetization vector field configurations within samples and devices. A systematic parameter space exploration—varying for example material parameters, temperature, or sample geometry—leads to the creation of many thousands of field configurations that need to be sighted and classified. This task is usually carried out manually, for example by looking at a visual representation of the field configurations. We report that it is possible to automate this process using an unsupervised machine learning algorithm, greatly reducing the human effort. We use a combination of convolutional auto-encoder and density-based spatial clustering of applications with noise (DBSCAN) algorithm. To evaluate the method, we create the magnetic phase diagram of a FeGe disc as a function of changing external magnetic field using computer simulation to generate the configurations. We find that the classification algorithm is accurate, fast, requires little human intervention, and compares well against the published results in the literature on the same material geometry and range of external fields. Our study shows that machine learning can be a powerful tool in the research of magnetic materials by automating the classification of magnetization field configurations.

Machine Learning Provides Fracture Analysis, Mapping for CCUS

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214996, “Machine-Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin,” by Guoxiang Liu, SPE, US Department of Energy, and Abhash Kumar and William Harbert, SPE, Contractors, et al. The paper has not been peer reviewed. _ The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis. Introduction This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives: - Improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way - Enable the optimization of carbon-storage reservoirs by creating a capability for real-time forecasting of the behavior of such reservoirs - Improve the ability to understand and communicate subsurface behavior during carbon-storage operations to nonexperts Methodology In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks. A fracture network encompasses natural fractures, induced or hydraulic fractures, and the dynamic response of such fractures, culminating in a comprehensive understanding of this system. Because of the wide range of resolutions and scales of the data sets, the fracture network was mapped into 19 distinct time windows over a 3-year injection period by analyzing pumping data, which included CO2 injection and bottomhole pressure. Passive seismic data (microseismic) were compartmentalized into these time windows, similar to the industry practice of compartmentalizing microseismic data into separate stages for high-pressure fluid- injection activities (i.e., induced fracturing). In each of these time windows, the b-value of the associated microseismic population was estimated. Furthermore, event-occurrence time and distance from the treatment well were used to identify the distinct triggering front of microseismicity likely associated with pre-existing fractures and faults within the reservoir. Seismogenic b-value and diffusivity analyses were applied to identify triggered fronts for further clustering and fracture-plane and 3D fracture-distribution analysis. The potential of several unsupervised ML algorithms was leveraged to identify the spatial clusters of microseismic events within each triggering front of individual time windows by following the work flow described in Fig. 1 of the complete paper. Furthermore, these ML techniques were implemented to determine the best-fitting surface for each spatial cluster of microseismicity to infer the directional and spatial distributions of fractures.

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims. In this paper, we analyze raw data taken from 16S rRNA gene sequencing from three studies, including stool samples from healthy control, patients with adenoma, and patients with colorectal cancer. Firstly, we use network-based methods to reduce dimensions of the dataset and consider only the most important features. In addition, we employ supervised machine learning algorithms to make prediction. Results show that graph-based techniques reduces dimen-sion from 255 up to 78 features with modularity score 0.73 based on different centrality measures. On the other hand, projection methods (non-negative matrix factorization and principal component analysis) reduce dimensions to 7 features. Furthermore, we apply supervised machine learning algorithms on the most important features obtained from centrality measures and on the ones obtained from projection methods, founding that the evaluation metrics have approximately the same scores when applying the algorithms on the entire dataset, on 78 feature and on 7 features. This study demonstrates the efficacy of graph-based and projection methods in the interpretation for 16S rRNA gene sequencing data. Supervised machine learning on refined features from both approaches yields comparable predictive performance, emphasizing specific microbial features-bacteroides, prevotella, fusobacterium, lysinibacillus, blautia, sphingomonas, and faecalibacterium-as key in predicting patient conditions from raw data.

Polyolefin ductile-brittle transition temperature predictions by machine learning

Polymers show a transition from ductile-to brittle fracture behavior at decreasing temperatures. Consequently, the material toughness has to be determined across wide temperature ranges in order to determine the Ductile-Brittle Transition Temperature This usually necessitates multiple impact experiments. We present a machine-learning methodology for the prediction of DBTTs from single Instrumented Puncture Tests Our dataset consists of 7,587 IPTs that comprise 181 Polyethylene and Polypropylene compounds. Based on a combination of feature engineering and Principal Component Analysis, relevant information of instrumentation signals is extracted. The transformed data is explored by unsupervised machine learning algorithms and is used as input for Random Forest Regressors to predict DBTTs. The proposed methodology allows for fast screening of new materials. Additionally, it offers estimations of DBTTs without thermal specimen conditioning. Considering only IPTs tested at room temperature, predictions on the test set hold an average error of 5.3°C when compared to the experimentally determined DBTTs.

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches

The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.

Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

PurposeThis study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.Design/methodology/approachThis study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.FindingsThrough the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.Practical implicationsThis study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.Social implicationsThis study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.Originality/valueThis study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Possibilities of identifying members from Milky Way satellite galaxies using unsupervised machine learning algorithms

Possibilities of identifying members from Milky Way satellite galaxies using unsupervised machine learning algorithms

BioisoIdentifier: an online free tool to investigate local structural replacements from PDB

Within the realm of contemporary medicinal chemistry, bioisosteres are empirically used to enhance potency and selectivity, improve adsorption, distribution, metabolism, excretion and toxicity profiles of drug candidates. It is believed that bioisosteric know-how may help bypass granted patents or generate novel intellectual property for commercialization. Beside the synthetic expertise, the drug discovery process also depends on efficient in silico tools. We hereby present BioisoIdentifier (BII), a web server aiming to uncover bioisosteric information for specific fragment. Using the Protein Data Bank as source, and specific substructures that the user attempt to surrogate as input, BII tries to find suitable fragments that fit well within the local protein active site. BII is a powerful computational tool that offers the ligand design ideas for bioisosteric replacing. For the validation of BII, catechol is conceived as model fragment attempted to be replaced, and many ideas are successfully offered. These outputs are hierarchically grouped according to structural similarity, and clustered based on unsupervised machine learning algorithms. In summary, we constructed a user-friendly interface to enable the viewing of top-ranking molecules for further experimental exploration. This makes BII a highly valuable tool for drug discovery. The BII web server is freely available to researchers and can be accessed at http://www.aifordrugs.cn/index/. Scientific Contribution: By designing a more optimal computational process for mining bioisosteric replacements from the publicly accessible PDB database, then deployed on a web server for throughly free access for researchers. Additionally, machine learning methods are applied to cluster the bioisosteric replacements searched by the platform, making a scientific contribution to facilitate chemists’ selection of appropriate bioisosteric replacements. The number of bioisosteric replacements obtained using BII is significantly larger than the currently available platforms, which expanding the search space for effective local structural replacements.Graphical

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

The air pollution caused by greenhouse gas emissions, particularly carbon dioxide (CO2), is a significant environmental concern that impacts air quality and contributes to global warming. The transportation sector plays a pivotal role in this issue, being a major contributor to CO2 emissions. In light of this situation, this article proposes a methodology that utilizes a supervised learning algorithm to estimate CO2 emissions and compare vehicles fueled with ethanol and gasoline. Additionally, the solution adopts an online, unsupervised machine learning algorithm to identify data outliers and improve the confidence in the results. Furthermore, this work incorporates the concept of digital twins, using virtual models of vehicles to carry out more extensive pollution simulations and allowing the simulation of various types of vehicles and the modeling of realistic traffic scenarios. A supervised machine learning approach was adopted to infer emission data in the model, allowing more comprehensive and meaningful comparisons between real-world and simulated measurements. The performed analyses of pollution emissions for different speeds and sections of routes demonstrate that CO2 emissions from ethanol were significantly lower than those from gasoline, favoring more sustainable fuels even in combustion engine vehicles. Adopting cleaner fuels is perceived as crucial to mitigate the negative effects of climate change, with plant-based fuels like ethanol being crucial during the transition from fossil fuels to a more sustainable vehicular landscape.

Privacy-Preserving Non-Negative Matrix Factorization with Outliers

Non-negative matrix factorization is a popular unsupervised machine learning algorithm for extracting meaningful features from inherently non-negative data. Such data often contain privacy-sensitive user information. Additionally, the dataset can contain outliers, which may lead to extracting sub-optimal features from the data. It is, therefore, necessary to address these two issues while analyzing privacy-sensitive data that may contain outliers. In this work, we develop a non-negative matrix factorization algorithm in the privacy-preserving framework that (i) considers the presence of outliers in the data, and (ii) can achieve results comparable to those of the non-private algorithm. We design our method in such a way that one has the control to select the degree of privacy grantee based on the required utility gap. We show the effectiveness of our proposed algorithm’s performance on six real and diverse datasets. The experimental results show that our proposed method can achieve a performance that closely approximates the performance of the non-private algorithm under some parameter choices, while ensuring strict privacy guarantees.

Система анализа тональности текста на телугу на основе нового пассивно-агрессивного классификатора с нечетким взвешиванием

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.

Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning.

Supervised and unsupervised machine learning algorithms are routinelyapplied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.

MACHINE LEARNING CAN PREDICT DIFFICULTY IN ANTERIOR APPROACH TOTAL HIP ARTHROPLASTY TO IMPROVE PATIENT SAFETY AND SURGICAL TRAINING

Anterior approach total hip arthroplasty (AA-THA) has a steep learning curve, with higher complication rates in initial cases. Proper surgical case selection during the learning curve can reduce early risk. This study aims to identify patient and radiographic factors associated with AA-THA difficulty using Machine Learning (ML).Consecutive primary AA-THA patients from two centres, operated by two expert surgeons, were enrolled (excluding patients with prior hip surgery and first 100 cases per surgeon). K- means prototype clustering – an unsupervised ML algorithm – was used with two variables - operative duration and surgical complications within 6 weeks - to cluster operations into difficult or standard groups.Radiographic measurements (neck shaft angle, offset, LCEA, inter-teardrop distance, Tonnis grade) were measured by two independent observers. These factors, alongside patient factors (BMI, age, sex, laterality) were employed in a multivariate logistic regression analysis and used for k-means clustering. Significant continuous variables were investigated for predictive accuracy using Receiver Operator Characteristics (ROC).Out of 328 THAs analyzed, 130 (40%) were classified as difficult and 198 (60%) as standard. Difficult group had a mean operative time of 106mins (range 99–116) with 2 complications, while standard group had a mean operative time of 77mins (range 69–86) with 0 complications. Decreasing inter-teardrop distance (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.95–0.99, p = 0.03) and right-sided operations (OR 1.73, 95% CI 1.10–2.72, p = 0.02) were associated with operative difficulty. However, ROC analysis showed poor predictive accuracy for these factors alone, with area under the curve of 0.56. Inter-observer reliability was reported as excellent (ICC &gt;0.7).Right-sided hips (for right-hand dominant surgeons) and decreasing inter-teardrop distance were associated with case difficulty in AA-THA. These data could guide case selection during the learning phase. A larger dataset with more complications may reveal further factors.

Measurement of Human Urine Specific Gravity Using Nanoplasmonics: A Paradigm Shift from Scales to Biosensors

AbstractUrine Specific Gravity (USG) is a direct indicator of the osmolarity of the urine and therefore it can be considered as a nonspecific marker of several underlying diseases which result in changes in hydration levels of the body. Here, a biosensor based on the principle of localized surface plasmon resonance (LSPR) is developed, which utilizes its refractive index sensing properties to measure USG with a sensitivity of 79.21 nm USG−1unit. Additionally, the sensor can measure the serum protein content within the urine. Traditionally, handheld refractometers are used to measure USG which are operated as calibrated refractive index scales rather than a sensor. A simple experiment demonstrating the advantage of a sensor over scale, with LSPR as the transduction method, is also conducted to highlight the enhanced sensitivity of a sensor over a scale. Finally, analysis of results with an unsupervised machine learning algorithm, principal component analysis (PCA), demonstrate the feasibility of automating or perhaps adding artificial intelligence to such sensors, thereby exemplifying a potential paradigm shift from refractive index scales to sensors in USG measurement.