Bayes Theorem Research Articles

Use of the Bayesian statistics and the product of probabilities in the ionizing radiation field

Purpose1) To show the validation criteria of the product of probabilities (PPs). Bayes’ theorem (BT) is widely used in the ionizing radiation field; and 2) To demonstrate that terms P(B|A) and P (A|B) in the BT are not new probabilistic metrics, but the own respective probabilities of B and An events. Methods and materialsMathematical derivations based on strong probabilistic foundations, and with their respective illustrations. Resultsa) Determination of the validationcriteria of the PPs; and b) There is demonstration of incoherencies of the BT. Conclusions1) The tumor control probability (TCP) of independent radiation oncology treatments to patients with more than one target, calculated as the product of individual TCPs is an incoherent determination; 2) Given previously explained about BT terms, the use of this theorem should be re-considered; and 3). P (A∩B)P(A)*P(B) only when N × P(A) and N × P(B) are a subset of a population N, and if A and B are independent and simultaneous events.

Estimation of HIV Prevalence among the Female Population in South India: A Bayesian Approach

Introduction: The HIV Sentinel Surveillance (HSS) conducted by National AIDS Control Organization (NACO) is the predominant data source for HIV estimations in India. While the HSS targets the key populations at risk of HIV infection, the National Family Health Survey (NFHS) measures the community- based HIV prevalence. Improvised HIV estimates in India were attributed to the HIV prevalence data obtained from the NACO-HSS and NFHS.
 Methods: Bayesian analysis was performed to determine the state-level prevalence of HIV among females in seven South Indian States. The analysis involved plotting the prior, likelihood, and posterior distributions, facilitating a visual assessment of the data. The HIV prevalence among females calculated from the NFHS (2015-16) survey data was used for prior distributions. HIV prevalence among pregnant women obtained from the HIV Sentinel Surveillance 2019 was used for likelihood. Bayesian analysis was performed using the R programming (RStudio 2022.02.0). A posterior probability distribution was obtained using the prior distribution and the likelihood by applying the Bayes theorem. Graphical representation was achieved through R's plotting functions. Kerala and Pondicherry were not included in the analysis due to zero or very low prevalence reported in both NFHS and HSS.
 Results: The Bayesian estimates of HIV prevalence among females were 0.38 % [95% CI:0.29 - 0.47] in Andhra Pradesh, 0.28 [95% CI:0.23 - 0.35] in Karnataka, 0.27 [95% CI:0.20 - 0.34] Odisha, 0.27 % [95% CI:0.19 - 0.36] in Telangana and 0.19 [95% CI:0.15 - 0.24] in Tamil Nadu.
 Conclusion: Bayesian techniques present a versatile and robust strategy for modelling and analysing HIV- related data, offering a flexible and powerful approach to data analysis.

Profession recommendation based on multiple intelligence for high school students

One of the problems students often face is the lack of understanding of their interests and talents which will cause confusion in making future study choices and career plans. Career selection is an expression of personality into the world of work followed by identification of certain occupational stereotypes. Eight intelligences according to dr. Howard Gardner is linguistic, logical, mathematical, visual spatial, musical, kinesthetic, interpersonal, intrapersonal, and naturalist. This research purpose is to develop a system that produces information and professional recommendations that are in accordance with multiple intelligences of prospective high school students using a combination of Bayes' theorem and weighted product (WP) method. User’s preference value is calculated using the Bayes Theorem method to give each multiple intelligence value which is a criterion. WP method calculation to find professions that match user preferences. Weight of each criterion needed in WP method is calculated by Rank Order Centroid method. WP calculation will produce a ranking of 3 professions according to the input from the user. From system testing results that compared to results from experts, an accuracy of 67.33% is obtained. Based on the accuracy value, it can be said that the accuracy level system is quite good.

Vibration-based Bayesian FE-model updating of a curved approaching span of the Indiano Bridge

The development of accurate finite element (FE) models of existing bridges represents a challenging issue due to the uncertainties on boundary conditions (e.g., friction of the supports and their behavior with the traffic loads, out of plumb of piers, soil-structure interaction, etc.), material properties and damping estimations. Full-scale measurements are usually employed for updating a preliminary FE model through deterministic methods, although probabilistic approaches should be preferable due to the non-negligible uncertainties present in both modeling and measurements.This work deals with the Bayesian FE-model updating of a curved approaching span of the Indiano Bridge (Florence, Italy) through full-scale vibration tests. The case study is represented by a steel/concrete composite deck slab bridge with a span of about 25 m. A procedure based on the Bayes theorem has been developed and tested on the considered case study incorporating both model uncertainties and measurement errors.

A Bayesian Approach to Atmospheric Circulation Regime Assignment

Abstract The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years. Significance Statement Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.

Comparative Study of K-NN, Naive Bayes and SVM for Face Expression Classification Techniques

Abstract Classification is a data mining technique used to predict group membership for data instances within a given dataset. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. This comparative study focuses on the application of K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machines (SVM) classification techniques in the context of face expression recognition. The study aims to evaluate and compare the performance of these techniques in accurately classifying facial expressions, their advantages and disadvantages. Facial expression recognition is an important area in computer vision and emotion analysis. It involves identifying and classifying different facial expressions such as happiness, sadness, anger, fear, surprise and disgust. Accurate classification of facial expressions is crucial for various applications, including human-computer interaction, affective computing and psychological research. K-NN is a non-parametric algorithm that classifies data based on the majority class of its K-nearest neighbors. Naive Bayes is a probabilistic algorithm that makes classification decisions based on the Bayes theorem and assumes independence between features. SVM is a machine learning algorithm that constructs a hyperplane to separate different classes of data. Dataset such as Affectnet is used to conduct the study including labeled facial expression images. Three algorithms are tested on this dataset to evaluate their performance in correctly classifying facial expressions. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the classification performance of each technique. Software used for conducting the experiments is Python.

Simulation Model for Predicting Banking Customers Satisfaction Using Chi-Square Test & Naïve Bayes Classifier

Today, it is necessary for all Public, Private and Government bank to find their banking customers satisfaction ratio depending on feedback provided by the customers. These research paper is useful for finding Personal Characteristics and Banking Behavior of Customers. The study uses primary data which was collected from 550 responders. The responders are taken from different profession and different age group. The bank can offer different kind of services like ATM, Net banking, Mobile banking and so on. The objective of the paper is to find the result of customer satisfaction. The result is found by Non- probabilistic technique chi- Square test and probabilistic technique Naïve Bayes Classifier. The chi- square test (symbolized as χ2) is basically done by analyzing the data of the agreed upon method for the d ifference between variables. It is usually a comparison of two statistical data. Naive Bayes algorithm is based on Bayes theorem and is used to solve classification problems. The result of study indicates both positive and negative influence on banking customer behavior. Keywords Simulation, chi-square test, Naïve Bayes Classifier, demographic segmentation, customer behaviors.

Longitudinal modeling of cardiac function to identify trajectories with differential risk of heart failure

Abstract Background Impairments in left ventricular (LV) systolic and diastolic function are common precursors of clinical heart failure (HF) and underlie HF risk. Little data exist regarding trajectories of cardiac function in mid- to late-life, when HF risk is greatest, and the extent to which such trajectories may differentially relate to risk of HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Purpose To (1) identify multidimensional trajectories of cardiac function from mid- to late-life using serial echocardiographic data, (2) apply the resulting models to predict trajectory membership in a separate sample, and (3) determine associations between assigned trajectory and incident HFpEF and HFrEF. Methods We used a Bayesian nonparametric trajectory approach to jointly model longitudinal measures of LV ejection fraction (LVEF) and E/A ratio among 747 participants in two longitudinal cohort studies (the Atherosclerosis Risk in Communities [ARIC] study and Jackson Heart Study [JHS]) who underwent ≥2 echocardiograms in 2000-04 (mean age 65±5 years), 2011-2013 (age 75±5 years), and 2018-19 (age 81±5 years). Using the trajectories inferred from this modeling, we applied Bayes theorem to assign 4,419 ARIC-only participants to their most likely trajectory using single time-point measures of LVEF and E/A ratio acquired in 2011-2013 (age 75±5 years). These assignments were treated as a categorical variable in subsequent analysis. In ARIC-only participants, we used Cox proportional hazards models adjusted for age, sex, obesity, hypertension, diabetes, chronic kidney disease, and coronary heart disease to assess the relationship between assigned trajectory and incident HF. Results In the discovery set (n=747), we identified six trajectories of cardiac function based jointly on longitudinal changes in LVEF and E/A ratio (Figure 1). The pink (prevalence 50%) and light green (17%) trajectories showed increasing LVEF and decreasing E/A ratio with increasing age, while the red trajectory (22%) demonstrated a lack of increase in LVEF. The dark green trajectory (4%) demonstrated progressive LVEF decline, the orange trajectory (2%) steep LVEF decline and rise in E/A ratio, and the blue trajectory (4%) rising E/A ratio despite increasing in LVEF. Analysis of the ARIC-only participants (n=4,419) showed that trajectory membership was differentially associated with risk of incident HFpEF and HFrEF (Figure 2). Compared to the pink trajectory, membership in the orange trajectory was associated with incident HFpEF and HFrEF, the red and the dark green trajectory associated with HFrEF alone, and the blue trajectory with HFpEF alone. Conclusion(s) These results demonstrate the feasibility of identifying trajectories of cardiac function with multidimensional longitudinal data and may have implications for HF risk prediction.

Is Drug Delivery System a Deterministic or Probabilistic Approach? A Theoretical Model Based on the Sequence: Electrodynamics–Diffusion–Bayes

Commonly, it is accepted that oncology treatment would yield outcomes with a certain determinism without any quantitative support or mathematical model that establishes such determinations. Nowadays, with the advent of nanomedicine, the targeting drug delivery scheme has emerged, whose central objective is the uptake of nanoparticles by tumors. Once they are injected into the bloodstream, it is unclear as to which process governs the directing of nanoparticles towards the desired target, deterministic or stochastic. In any scenario, an optimal outcome, small toxicity and minimal dispersion of drugs is expected. Commonly, it is expected that an important fraction of them can be internalized into tumor. In this manner, due to the fraction of nanoparticles that have failed to uptake, the success of the drug delivery scheme might be at risk. In this paper, a theory based on the sequence electrodynamics–diffusion–Bayes theorem is presented. The Bayesian probability that emerges at the end of the sequence might be telling us that dynamical processes based on the injection of electrically charged nanoparticles might be dictated by stochastic formalism. Thus, rather than expecting a deterministic process, the chain of events would convert the drug delivery scheme to be dependent on a sequence of conditional probabilities.

Diagnosis of Parasitic Diseases in Animals Cat Using Bayes Theorem Method

Cats are one of the most popular pets in the world, including Indonesian people who like to keep cats as pets, and even become a hobby for cat lovers. Diseases that often attack cats are caused by parasites, namely worms and fleas. Parasites that attack cats are grouped into two, namely ectoparasites and endoparasites. expert system which is a computer program , which is able to store knowledge and rules like an expert . With the help of an expert system, someone who is lay or not an expert in a particular field will be able to answer questions, solve problems, and make decisions that are usually made by an expert . The Bayes Theorem method can be applied to diagnose parasitic diseases in cats based on input symptoms chosen by the users, the system can perform analysis based on predetermined rules or knowledge base. Based on the probability value of each symptom and disease that has been made, the system can diagnose parasitic diseases in cats with different accuracy results, the highest value or percentage which is the result of the diagnosis of the parasitic disease. From the results of trials conducted by the expert system for diagnosing parasitic diseases in cats using the Bayes Theorem method, the highest value was obtained, namely the type of parasitic disease Flea Disease (P03) with a percentage of 38.66%.

Medical Diagnosis Reimagined as a Process of Bayesian Reasoning and Elimination.

This article delves into the interface between the art of medical diagnosis and the mathematical foundations of probability, the Bayes theorem. In a healthcare ecosystem witnessing an artificial intelligence (AI)-driven transformation, understanding the convergence becomes crucial for physicians. Contrary to viewing AI as a mysterious "black box," we demonstrate how every diagnostic decision by a medical practitioner is, in essence, Bayesian reasoning in action. The Bayes theorem is a mathematical translation of systematically updating our belief: it quantifies how an additional piece of information updates our prior belief in something. Using a clinical scenario of Kartagener syndrome, we showcase the parallels between a physician's evolving diagnostic thought process and the mathematical updating of prior beliefs with new evidence. By reimagining medical diagnosis through the lens of Bayes, this paper aims to demystify AI, accentuating its potential role as an enhancer of clinical acumen rather than a replacement. The ultimate vision presented is one of harmony, where AI serves as a symbiotic partner to physicians, with the shared goal of holistic patient care.

A dynamic evaluation method for slope safety with monitoring information based on a hybrid intelligence algorithm

The variability of soil or rock parameters based on poor information results in an uncertain change in slope failure probability. To solve the negative impact of slope reliability analysis introduced by variability of soil or rock parameters, this article proposes a hybrid intelligence algorithm for analyzing the slope failure probability with Bayes theorem, integrating a one-dimensional convolutional neural network (1D-CNN), and executing Markov chain Monte Carlo (MCMC) simulation with multidepth displacements. We calculate the failure probability through 1D-CNN prediction. The 1D-CNN learns from data generated by numerical models to directly relate input variables to output variables toward obtaining an accurate model of the response surface. The resulting soil or rock parameters are described as probability distributions, which are updated by MCMC simulations based on the multidepth displacement time series. A case study with monitoring displacement along the slope depth is presented to illustrate the effectiveness of our proposed methodology. We find that our method can update the failure probability value and identify the location of potential sliding surfaces with the time series of multidepth displacement. We also find that incorporating changes in Poisson's ratio and elastic modulus seems to have a significant influence on the slope safety evaluation.

Addressing Non-ignorable Panel Attrition Using External Population Data: Analysis of Demographic Events From Survey Data

Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event probabilities in the absence of additional assumptions. The paper introduces an adjustment procedure based on Bayes Theorem that directly addresses the problem of nonignorable dropout. It uses population information external to the survey sample to convert estimates of event probabilities and marginal effects of covariates on them that are conditional on retention in the longitudinal data to unconditional estimates of these quantities. In many plausible and verifiable circumstances, it produces estimates of the marginal effect of covariates closer to the true unconditional quantities than the conditional estimates obtained from estimation using the survey data alone.

SPSNet: Boosting 3D point-based object detectors with stable point sampling

Currently, the primary focus of 3D point-based detectors is to enhance their performance by primarily increasing the recall rate of foreground points at the semantic level. However, this approach is suboptimal. On one hand, ground-truth annotations of 3D bounding boxes are inherently ambiguous due to occlusions, signal distortion, or manual annotation errors, which can obscure genuine semantic information. On the other hand, the saliency of each point varies. In this paper, we propose SPSNet based on Bayes theorem to explore the mapping relationship between the sampled point and the corresponding bounding box, which we call point stability. This is a plug-and-play module and used as the basis for downsampling instead of relying on semantic information obtained through hard supervision of 3D bounding boxes. We incorporate the proposed method into the most popular point-based detector, IA-SSD, called SPSNet-IA. On the challenging KITTI validation set, with only changes to the downsampling strategy, it obtains 1.18, 2.78 and 2.96 gains for the metric of AP3D on the easy, moderate and hard levels, respectively. In addition, SPSNet-IA outperforms all published point-based approaches by a large margin and ranks 1st among single-modal methods. We also design qualitative and quantitative experiments to explain the meaning of stability learned from SPSNet. These additional experiments demonstrate that SPSNet is not only a performance-enhancing model but also a saliency analysis model. The code is available at https://github.com/AlanLiangC/SPSNet.git.

Diagnosis Accuracy for Compartment Syndrome: A Systematic Review and Meta-Analysis.

To evaluate whether published studies support basing the diagnosis of compartment syndrome of the lower leg on clinical findings, intracompartmental pressure (ICP) monitoring, or both. A PubMed/MEDLINE, Web of Science, and Embase search of the English literature from 1966 to February 2022 was performed. This used "lower extremity" or "leg" or "tibia" and "compartment syndrome" and "pressure" as the subjects. A manual search of the bibliographies was performed and cross-referenced with those used to formulate the American Academy of Orthopaedic Surgeons clinical practice guidelines. Inclusion criteria were traumatic tibia injuries, presence of data to calculate the sensitivity, specificity, positive and negative predictive values of clinical findings and/or pressure monitoring, and the presence or absence of compartment syndrome as the outcome. A total of 2906 full articles were found, of which 63 were deemed relevant for a detailed review. Seven studies met all eligibility criteria. The likelihood ratio form of Bayes theorem was used to assess the discriminatory ability of the clinical findings and ICP monitoring as tests for compartment syndrome. The predictive value for diagnosing acute compartment syndrome was 21% and 29% for the clinical signs and ICP, respectively. When combining both, the probability reached 68%. The use of ICP monitoring may be helpful when combined with a clinical assessment to increase the sensitivity and specificity of the overall diagnosis. Previously accepted individual inference values should be revisited with new prospective studies to further characterize the statistical value of each clinical finding. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Assessment of multiple fecal contamination sources in surface waters using environmental mitochondrial DNA metabarcoding

Waterborne diseases are transmitted to humans through the fecal contamination of water, where homeothermic species are the main reservoir. Fecal indicator bacteria (FIB) are often used to determine the occurrence of fecal contamination. However, FIB cannot provide the source of fecal contamination. Furthermore, as fecal inputs and contamination could originate from multiple sources (e.g., human, livestock, wildlife), multiple source tracking markers are required to identify fecal sources. From a previous study, we developed a mitochondrial DNA (mtDNA) metabarcoding approach to assess the presence of multiple homeotherms in four surface waters. Here, we have broadened our approach by sampling 86 surface water samples from the L'Assomption River and Ville-Marie watersheds (Province of Quebec, Canada). Fecal coliform levels were higher than the expected sanitary recommendations for recreational water (> 200 CFU/100 mL) in 73 % samples. The occurrence of mtDNA from human, livestock, domestic animals, wild mammals and wild birds was found in 40–88 % of the samples. Multivariate analyses showed significant covariations between homeothermic taxa and fecal coliforms, enterococci, β-D-glucuronidase, conductivity, the human-specific Bacteroidales Hf183 genetic marker, and the human population, in the watersheds of L'Assomption River (p = 0.001) and Ville-Marie (p = 0.015) (Province of Quebec, Canada). Through the application of Bayes Theorem, it was determined that fecal coliforms co-occurred with the detection of bovine, beaver, robin and chicken mtDNA in 100 % of cases in the L'Assomption River watershed, and human mtDNA co-occurred with fecal coliforms in 93 % and 76 % of cases in L'Assomption River watershed and Ville-Marie sub-catchment, respectively. This study suggests that fecal contamination could be the result of multiple species, among which some wild animals may contribute to fecal inputs in surface waters, resulting in potential risk to human health. This reinforces the necessity of using the mtDNA metabarcoding method to monitor multi-animal species.

Using real-time ascertainment rate estimate from infection and hospitalization dataset for modeling the spread of infectious disease: COVID-19 case study in the Czech Republic.

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.

Probability of sporadic lymphangioleiomyomatosis in women presenting with spontaneous pneumothorax

BackgroundSporadic lymphangioleiomyomatosis (S-LAM) is a rare low-grade neoplasm of young women characterized by multiple pulmonary cysts leading to progressive dyspnea and recurrent spontaneous pneumothorax (SP). The diagnosis of S-LAM may be delayed by several years. To reduce this delay, chest computed tomography (CT) screening has been proposed to uncover cystic lung disease in women presenting with SP. However, the probability to discover S-LAM in this population has not been determined precisely. The aim of this study was to calculate the probability of finding S-LAM in women presenting with (a) SP, and (b) apparent primary SP (PSP) as first manifestation of S-LAM.MethodsCalculations were made by applying the Bayes theorem to published epidemiological data on S-LAM, SP and PSP. Each term of the Bayes equation was determined by meta-analysis, and included: (1) the prevalence of S-LAM in the general female population, (2) the incidence rate of SP and PSP in the general female population, and (3) the incidence rate of SP and apparent PSP in women with S-LAM.ResultsThe prevalence of S-LAM in the general female population was 3.03 per million (95% confidence interval 2.48, 3.62). The incidence rate of SP in the general female population was 9.54 (8.15, 11.17) per 100,000 person-years (p-y). The incidence rate of SP in women with S-LAM was 0.13 (0.08, 0.20). By combining these data in the Bayes theorem, the probability of finding S-LAM in women presenting with SP was 0.0036 (0.0025, 0.0051). For PSP, the incidence rate in the general female population was 2.70 (1.95, 3.74) per 100,000 p-y. The incidence rate of apparent PSP in women with S-LAM was 0.041 (0.030, 0.055). With the Bayes theorem, the probability of finding S-LAM in women presenting with apparent PSP as first disease manifestation was 0.0030 (0.0020, 0.0046). The number of CT scans to perform in women to find one case of S-LAM was 279 for SP and 331 for PSP.ConclusionThe probability of discovering S-LAM at chest CT in women presenting with apparent PSP as first disease manifestation was low (0.3%). Recommending chest CT screening in this population should be reconsidered.

A novel Bayesian reconstruction of the configurational density of states

In this work, we present the development and implementation of a novel Bayesian method for the reconstruction of the density of states (DOS) of a system using energy data obtained from Monte Carlo simulations. This method uses a trial family of functions with adjustable parameters, which are optimized using the Bayes theorem. The measurements can be done in any ensemble with a known distribution function, which significantly helps to overcome energy traps and explore the conformation space thoroughly. We apply our algorithm on a test Potts model system and find that our implementation can find the correct DOS in a reasonable amount of time. Moreover, if the trial function is suitable enough, the DOS found by the algorithm is very close to the actual DOS.

Penerapan Metode Teorema Bayes Dalam Mendiagnosa Penyakit Autoimun

Autoimmune diseases are caused by the failure of the immune system to attack the body itself. According to data from the US Department of Health and Human Services, more than 23.5 million Americans have an autoimmune disease, which is difficult to diagnose because of the variety of symptoms it presents. Therefore, the development of mechanisms to identify autoimmune disorders is essential. One of the developing technologies in this field is the use of expert systems in diagnosing diseases. An expert system is a system developed by experts using science-based technology. In order to use it effectively, proper methods are needed, such as the Bayes Theorem approach, described by Thomas Bayes, a priest. The Bayes Theorem approach explains the relationship between the probability of event A and event B based on available information. This study attempts to facilitate the diagnosis of autoimmune diseases by using an expert system and Bayes' Theorem technique. With a confidence level of 0.57 or 57%, the examination results show that the patient suffers from an autoimmune disease of the type Hemolytic Anemia (HA) based on the patient's input