Accuracy Rate Research Articles

Efficient machine learning approach for accurate free-energy profiles and kinetic rates.

The computational exploration of reactive processes is challenging due to the requirement of thorough sampling across the free energy landscape using accurate ab initio methods. To address these constraints, machine learning potentials are employed, yet their training for this kind of problem is still a laborious and tedious task. In this study, we present an efficient approach to train these potentials by cleverly using a single batch of unbiased trajectories that avoid the pitfalls of trajectories artificially biased along a suboptimal collective variable. This strategy, when integrated with current enhanced sampling techniques, allows to obtain free energy profiles and kinetic rates of ab initio quality, yet dramatically reducing the computational cost.

NP student knowledge and perceptions of medical marijuana.

This study aimed to measure NP students' knowledge and perception of medical marijuana (MM) and determine whether they need additional education on the topic. A cross-sectional, descriptive study was implemented. NP students were asked to complete a survey that assessed their knowledge of MM, including adverse reactions and medical indications. Perceptions of recreational marijuana and MM were assessed through a 5-point Likert-scale questionnaire. Of the 230 NP students who completed all or part of the questionnaire, 115 met inclusion criteria and were included in results. Most students (94%) felt that MM should be legalized in all US states. Likewise, 97% felt that MM education should be included in NP program curricula; however, only 30% reported receiving in-depth education on the topic. Participants were asked to identify, among 18 provided medical conditions, those which were approved indications for MM use; accurate response rate was low, though cancer, pain, cachexia, nausea/vomiting, posttraumatic stress disorder, and epilepsy were identified correctly as indications by more than half of participants. Previous MM use and religious affiliation had an impact on knowledge and perceptions of MM. With an increasing number of states legalizing MM, NP programs must evaluate their current curricula and integrate content that addresses the gap in knowledge of MM mechanism of action, indications, and adverse reactions.

Optimizing prostate cancer detection in transition zone: an analysis of apparent diffusion coefficient values in prostate magnetic resonance imaging evaluation with Prostate Imaging Reporting and Data System (PI-RADS) assessment.

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for the early detection of clinically significant prostate cancer (csPCa). However, the achievement of accurate detection rates, particularly for transition zone (TZ) lesions, remains challenging. We investigated the relationship between apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions and csPCa within the TZ. We retrospectively evaluated TZ lesions in patients who underwent 3.0 Tesla MRI followed by MRI-targeted/transrectal ultrasound fusion biopsies (MRI-FBx). Fusion biopsies were performed for potentially cancerous lesions, defined as lesions with PI-RADS scores 3-5. We analyzed 196 lesions for which fusion biopsies were performed. The overall prostate cancer (PCa) detection rate was 53.6% (105/196); csPCa constituted 33.7% (66/196) of cases. The minimum ADC value was significantly lower for patients with csPCa (484.9±112.3 µm2/s) than for patients with benign histology or non-csPCa (P<0.001). Older age, higher initial prostate-specific antigen level, larger region of interest, and minimum and mean ADC values were associated with the presence of csPCa. Multivariate analysis indicated that only the minimum ADC value was an independent predictor of csPCa. Using a cutoff minimum ADC value <561 µm2/s to detect csPCa in TZ lesions increased the detection rate to 57.4% (54/94). The minimum ADC value provides substantial additional information regarding the presence of csPCa in the TZ, potentially improving the detection rates for lesions rated as PI-RADS 3-5 and informing the need for follow-up biopsies in areas that are initially cancer-free.

A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of ‘omics’ data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

The Evolutionary Interplay of Somatic and Germline Mutation Rates.

Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.

Dementia and Alzheimer's Disease Prevalence in Bulgaria During 2018: Nationally Representative Study.

This paper presents data analysis of a large dataset of outpatient records for the purpose of establishing accurate prevalence rates of dementia and Alzheimer's disease in Bulgaria. The research is motivated by the lack of accurate prevalence data as well as statistics about the actual number of people affected by dementia at national level. Health data from pseudonymized outpatient records (1,378,355) of 642,013 unique patients (Male 39.88%, Female 61.67%) with visits to neurologists and psychiatrists in 2018 was mapped to an OMOP CDM relational database. The size of this dataset is one of largest in the EU context. Prevalence of dementia for all age groups [30,100+] years is 1.61% (Male 0.62%, Female 0.99%) and Alzheimer' disease prevalence is 0.39% (Male 0.15%, Female 0.24%), where 24.34% of all the patients with dementia suffer Alzheimer's disease. The mean prevalence rates of dementia and Alzheimer's disease for ages above 64 are 9.52% (CI 95%, [6.98%, 12.04%]) and 1.04 % (CI 95%, [0.84%, 1.27%,]). A comparison with EU statistics yields 0.07% difference for dementia prevalence, while data visualization confirms the known patterns of prevalence with aging and gender specifics These results stimulate further research and support the development of a national strategy for dementia and Alzheimer's disease.

Analyzing the performance of ChatGPT in answering inquiries about cervical cancer.

To analyze the knowledge of ChatGPT about cervical cancer (CC). Official websites of professional health institutes, and websites created by patients and charities underwent strict screening. Using CC-related keywords, common inquiries by the public and comments about CC were searched in social media applications with these data, a list of frequently asked questions (FAQs) was prepared. When preparing question about CC, the European Society of Gynecological Oncology (ESGO), European Society for Radiotherapy and Oncology (ESTRO), and European Society of Pathology (ESP) guidelines were used. The answers given by ChatGPT were scored according to the Global Quality Score (GQS). When all ChatGPT answers to FAQs about CC were evaluated with regard to GQS, 68 ChatGPT answers were classified as score 5, and none of ChatGPT answers for FAQs were scored as 2 or 1. Moreover, ChatGPT answered 33 of 53 (62.3%) CC-related questions based on ESGO, ESTRO, and ESP guidelines with completely accurate and satisfactory responses (GQS 5). In addition, eight answers (15.1%), seven answers (13.2%), four answers (7.5%), and one answer (1.9%) were categorized as GQS 4, GQS 3, GQS 2, and GQS 1, respectively. The reproducibility rate of ChatGPT answers about CC-related FAQs and responses about those guideline-based questions was 93.2% and 88.7%, respectively. ChatGPT had an accurate and satisfactory response rate for FAQs about CC with regards to GQS. However, the accuracy and quality of ChatGPT answers significantly decreased for questions based on guidelines.

The effect of health literacy on patient compliance in patients to whom prostate biopsies were recommended

BackgroundTransrectal ultrasound-guided prostate biopsy (TRUS-Bx) is the gold standard diagnostic method for prostate cancer. In people with low health literacy, accurate and early diagnosis rates decrease, making it difficult to maintain health and compliance with treatment. In our study, we investigated how health literacy and sociocultural parameters affected compliance and awareness in patients with suspected prostate cancer, for whom TRUS-Bx was planned.MethodsIn the study, 98 male patients aged 50–80 years, recommended for TRUS-Bx, were included in our study. The data including age, prostate-specific antigen, prostate volume, digital rectal examination findings, education leveland area of residence of the patients included in the study were recorded. Health Literacy Survey-Turkey- Questionnaire 47 and Turkish Health Literacy Scale-32 forms were completed by the patients who agreed to participate in the study, and their scores were recorded. Patients scheduled for TRUS-Bx were divided into two groups: those who attended their appointments and underwent the biopsy, and those who did not attend their scheduled appointments. The effect of health literacy and other parameters on the TRUS-Bx requirement was examined between the two groups. Furthermore, 52 patients who underwent TRUS-Bx were divided into two groups as malignancy (malignant) detected and not-detected (benign) patients according to the pathology results, and the parameters were analyzed separately for these groups.ResultsThe education level of the patients who underwent the TRUS-Bx procedure was found to be statistically higher (p = 0.026). Health Literacy Survey-Turkey- Questionnaire 47 and Turkish Health Literacy Scale-32 scores were statistically significantly higher in the TRUS-Bx group (p = 0.001, p < 0.001, respectively). In the logistic regression analysis, education level, Health Literacy Survey-Turkey- Questionnaire 47 and Turkish Health Literacy Scale-32 were found to be important predictors for awareness of the requirement for TRUS-Bx.ConclusionThe study’s findings indicate that patients with higher health literacy and education levels were more likely to receive an early diagnosis and promptly proceed with the recommended TRUS-Bx after visiting a urologist.

Accuracy of gravity based automatic infusion system applied to chemoport for intravenous infusion.

191 Background: When a vesicant drug is administered to the peripheral vein, a gravity drip method rather than an peristaltic infusion pump is recommended. In addition, it is reported that the gravity based pump has less particle generation than the peristaltic pump that injects fluid through constant peristaltic movement. However, it is pointed out that the disadvantage of gravity based pump is that the rate of administration can easily change according to the change in the location of the injection site. We evaluate the accuracy of new gravity based pump (pressure-sensitive speed control fluid injection device, Accudrip made by Hanvit MD ) at various injection rates and hanging heights common to hospital settings. Methods: The accuracy and safety of the intravenous (IV) flow controller were evaluated for patients receiving IV chemotherapy in the oncology department. It was verified whether the accurate injection rate could be maintained even in various fluid heights, fluid amounts, and types of anticancer drugs that could affect the injection rate. Various anticancer drugs, including immune checkpoint inhibitor, were mixed with 250 ml/500 ml of normal saline and administered for 30 min/60 min, and glucose 500 ml/1000 ml was administered for 60 min/120 min to measure accuracy, each progressed 25 times. Medical staff were surveyed on the ease and limitations of medical practice for each peristaltic infusion pump and new gravity based pump. In addition, side effects occurring in patients during fluid administration were recorded. Results: The mean degree of error in the estimated end time of gravity based automatic infusion system was 3.09% (2.09 ~ 4.10). The error of the gravity based automatic infusion system was statistically significant depending on the patient age (p=.001) and the infusion time (p=.016), but the degree of error according to the type of fluids (p=.302) and fluid concentration (p=.844) was not statistically significant. Among the subjects of the study, nurses' satisfaction with the application of gravity based pump was 4.10 (±0.62) out of 5, and among the general characteristics of nurses, age ( p&lt;.078), and working department (p&lt;.91) did not show a statistically significant difference, but work experience ( p &lt;.021) showed a statistically significant difference. Conclusions: The accuracy of Gravity based automatic infusion system was determined because the degree of error in the expected end time of infusion was not significant according to the infusion variable. In a survey of nurses who have used gravity based pump directly, it was found that the ease of use and satisfaction were similar to those of the conventional peristaltic pump. These findings will contribute greatly to the precise drug administration (over or under-administration) and minimization of adverse events in real-world hospital settings.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates. A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes. Twenty-three pre-identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal-Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts' occurrence ratings by means of the number of incoming reports. Over a 15-month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014-0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003-0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p=0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved. Our approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

TO EAT, OR NOT TO EAT? A PSYCHOANALYTIC VIEW ON THE FOOD‐PARENT

Research shows eating disorders increase the mortality rate: anorexia nervosa has the highest mortality rate and yet an accurate morbidity rate of bulimia nervosa remains hidden. However, research on the endopsychic structural dynamics that perpetuate in patients with eating disorders is scant. This essay depicts the use of Fairbairn's theory of endopsychic personality structure in understanding anorexia nervosa, binge eating disorder and bulimia nervosa. Fairbairn, of the independent group of British object relations theorists, provides a picture of the endopsychic structure based on the conscious and unconscious psychodynamics between partial ego/part‐object dyads. Using three case histories, the following pages illustrate the incessantly present endopsychic permutations of the aforementioned dynamics and the possibility of the entrapment in one of these or swinging from one to the other in eating disorders. The essay also shows that early traumatic experiences are present in the case history of individuals with any one of these eating disorders and despite their manifested behavioural differences, they result from the widening of fissures in the universal split in the psyche due to emotional and/or physical abuse. Finally, using Fairbairn's theory, the analysis in the essay explains the comorbidity of certain eating disorders and borderline personality structure.

Recognition of domestic &lt;em&gt;Felis Catus&lt;/em&gt; vocalisations using a computational associative model

The cat (Felis catus) lives around or with humans and is considered one of the most popular pets in the world. Domestic cats have an extensive and complex vocalisation repertoire, and little is known about the meaning of their vocalisations; these animal sounds have been studied to determine their behavioural characteristics. This work implemented a computational model for cat vocalisation as a signal process to characterise it. Additionally, a recognition model is proposed to identify and associate domestic cat vocalisations with cat intentions. Domestic cat vocalisations were studied by obtaining sounds from audio and video sources stored in the web cloud and processing signals (distress, hiss, howl, meow, fright, purr, sexual call, and trill vocalisations) that belong to some cat situations (soliciting attention, complaining, surprise, threatening, welcoming, fighting, feeling hungry, mating, etc.). In measured signals, environmental noise was removed using a computational algorithm to increase the effectiveness of the recognition process. As a first approach, cat vocalisation patterns were extracted using Welch's method for spectral density estimation, appreciating vocalisation frequency characteristics and statistics. Additionally, a similarity function to compare cat vocalisation signals was developed, excluding useless patterns that could introduce computational problems in the learning phase of the recognition model. Then, an associative memory, the Lernmatrix Thresholded model, was used to recognise the domestic cat vocalisation needed, and pair associations between sounds and moods related to the recognition process result were established, i.e., identifying cat intention through vocalisation by analysing the signals emitted. The experimental results show the performance of the thresholded Lernmatrix among different pattern recognition models, where accurate vocalisation recognition rates are obtained. This computational model emerges as a suitable tool that could be helpful in animal care and ethology science to characterise the behaviour of animals through their vocalisation.

Development of sensitive biomass xylan-based carbon dots fluorescence sensor for quantification detection Cu2+ in real water and soil

Copper ions (Cu2+) pose significant risks to both human health and the environment as they tend to accumulate in soil and water. To address this issue, an innovative method using biomass-derived fluorescent carbon dots (D-CDs) synthesized via a hydrothermal process, with xylan serving as the carbon source was developed. D-CDs solution exhibited remarkable sensitivity and selectivity as a fluorescence sensor for Cu2+, boasting a low detection threshold of 0.64 μM. In order to facilitate real-time monitoring of Cu2+, solid-state fluorescent nanofiber membrane (NFD-CDs) through electrospinning was engineered. Additionally, D-CDs demonstrated successful Cu2+ detection in various real water samples, including those sourced from Xuanwu Lake, the Yangtze River, tap water, and bottled water, with accurate recovery rates observed. As a result, this research introduces a dual-mode analytical system for onsite detection of Cu2+ in real scenarios. By harnessing biomass-derived fluorescent CDs materials and solid-state fluorescence sensors, this approach offers a promising solution for addressing the challenges associated with Cu2+ contamination.

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Abstract Achieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain knowledge and capturing non-linear relationships. Its ensemble nature also mitigates the impact of outliers. This approach combines physics-based equations with machine learning to enable more accurate ROP predictions in drilling operations. It enhances drilling efficiency, reduces expenses, and improves decision-making in the oil and gas sector. Extensive testing on actual drilling datasets has demonstrated outstanding performance compared to the multiple linear regression (MLR) method. With the increased R2 and zero P-value. positive findings show that this tool can benefit precise future ROP prediction in southern Iraqi oil well drilling.

Final state rescattering effects in axio-hadronic η and η′ decays

It has been long-understood that final state rescattering effects provide O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(1) corrections to hadronic meson decays rates, such as η → πππ and η′ → ηππ. Hence, one would expect that such effects would be just as important in axio-hadronic η and η′ decays, such as η(′) → ππa, where a is an axion or axion-like particle (ALP). And indeed they are, as we show in this paper by using the treatment of dispersion relations to include the effects of strong final state interactions in several axio-hadronic processes, namely, η(′) → π0π0a, η(′) → π+π−a, and η′ → ηπ0a. We also compute the perturbative, leading order decay rates for multiple ALP emission, such as in η(′) → π0aa, η′ → ηaa and η(′) → aaa, and briefly discuss the expected corrections from strong interactions and the processes that must be considered for an accurate rate estimation of these multi-ALP decay channels.

3D DenseNet with temporal transition layer for heart rate estimation from real-life RGB videos.

Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and the architecture of the deep learning models. In this paper, we develop a deep learning-based model leveraging the power of 3D convolutional neural networks (3DCNN) to extract temporal and spatial features that lead to an accurate heart rates estimation from RGB no pre-defined region of interest (ROI) videos. We propose a 3D DenseNet with a 3D temporal transition layer for the estimation of heart rates from a large-scale dataset of videos that appear more hospital-like and real-life than other existing facial video-based datasets. Experimentally, our model was trained and tested on this less controlled dataset and showed heart rate estimation performance with root mean square error (RMSE) of 8.68 BPM and mean absolute error (MAE) of 3.34 BPM. Moreover, we show that such a model can also achieve better results than the state-of-the-art models when tested on the VIPL-HR public dataset.

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.