Correct Decisions Research Articles

Comparative Analysis of Classification Algorithms for Heart Disease

Abstract: Heart is the main component of the human body and without it the body can’t function. It provides the flow of blood of different organs and body parts. It purifies the blood by removing the carbon dioxide(co2). It is also known as cardiovascular disease, it creates many risk factors for a human, including death. Heart disease is one of the most common causes of death around the world nowadays. Often, the enormous amount of information is gathered to detect diseases in medical science. All of the information is not useful but vital in taking the correct decision. Thus, it is not always easy to detect the heart disease because it required skilled knowledge or experiences about heart failures symptoms for an early prediction. Most of the medical dataset are dispersed, widespread and assorted. However data mining is a robust technique for extracting invisible, predictive and actionable information from the extensive databases. for identifying the possibility of heart disease in a patient. This work is justified by performing a comparative study and analysis using classification algorithms namely Logistic Regression, KNN, SVM, Decision Tree, and Random Forest are used at different levels of evaluation. Further, this research work is aimed towards identifying the best classification algorithm for identifying the possibility of heart disease in a patient. This work is justified by performing a comparative study and analysis using three classification algorithms namely Logistic Regression, KNN, SVM, Decision Tree and Random Forest are used at different levels of evaluation.

Credit Risk Analysis Using Fuzzy Logic with Machine Learning Models

Credit Risk is an important issue in the Banking Industry. Credit risk Prediction and assessment of credit is a difficult task for credit managers. Banking Industry has large amount of data related to the behavior of the customer and their credit history, but this raw data is not useful for making correct judgment in credit decisions. The banking industry is need of a correct credit decision making system, to distinguish between good customers and default customers. The data mining domain is suitable for assessing credit risk and making good decisions on credit. Feature selection is one of the main pre-processing step in the data mining. This paper use FuzzyRoughSetTheory (FRST) for finding feature subset. Four other feature selection methods are used to find the optimal feature subset. The four feature selection methods are Information Gain, Relief, Chi-Squared and Wrapper subset model. These different feature selection methods are compared in terms of accuracy and efficiency.

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, “Alex” cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (Nfd, Pfd, Kfd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R2 = 0.763, p = 0.013 for the model based on applied fertilizers (NPKF); R2 = 0.883, p < 0.01 for the model based on foliar diagnosis (NPKfd); R2 = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R2 = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R2 = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R2 = 0.717, compared to R2 = 0.763 for the F model in this study. The models obtained in this study, related to the “Alex” wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies.

Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator.

This paper considers the active fault isolation problem for a class of uncertain multimode fault systems with a high-dimensional state-space model. It has been observed that the existing approaches in the literature based on a steady-state active fault isolation method are often accompanied by a large delay in making the correct isolation decision. To reduce such fault isolation latency significantly, this paper proposes a fast online active fault isolation method based on the construction of residual transient-state reachable set and transient-state separating hyperplane. The novelty and benefit of this strategy lies in the embedding of a new component called the set separation indicator, which is designed offline to distinguish the residual transient-state reachable sets of different system configurations at any given moment. Based on the results delivered by the set separation indicator, one can determine the specific moments at which the deterministic isolation is to be implemented during online diagnostics. Meanwhile, some alternative constant inputs can also be evaluated for isolation effects to determine better auxiliary excitation signals with smaller amplitudes and more differentiated separating hyperplanes. The validity of these results is verified by both a numerical comparison and an FPGA-in-loop experiment.

Calibrating lane-changing models: Two data-related issues and a general method to extract appropriate data

Lane-changing is a routine but difficult driving task with important implications on traffic flow characteristics. Despite significant progresses in lane-changing decision modeling, lane-changing models are often improperly calibrated due to two issues related to trajectory data processing. First, the time of insertion (i.e., the time instant where a vehicle crossed the lane marking) is incorrectly considered as the lane-changing decision point, since the lane-changing decision is typically made earlier. Secondly, there is an imbalance between the number of non-lane-changing and lane-changing events, where non-lane-changing events typically dominate trajectory data. These issues can overestimate model performance and biased parameters. In this paper, we propose a method that combines (i) the wavelet transform method to pinpoint the correct lane-changing decision point, and (ii) a case–control design to systematically neutralize the dominance of non-lane-changing events in the data. The proposed method is applied to two NGSIM datasets to assess the performance of four representative lane-changing models. Results uncover that (i) lane-changing models are sensitive to various degrees of data imbalance, (ii) regardless of a driver’s decision time window (e.g., 1 s, 2 s, or 3 s), an analysis time window of 6 s will work reasonably well for evaluating the performance of a lane-changing model, while the optimal control-to-case ratio is 1:1; and (iii) when possible, a temporal discretization interval (i.e., an approximation of a driver’s typical decision time window) of 2 s should be preferred, while 3 s should be avoided. The proposed method also enabled us to outline a performance range for the selected lane-changing models.

Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany.

To date, there is no structured program for dermatoscopy training during residency in Germany. Whether and how much dermatoscopy training is acquired is left to the initiative of each resident, although dermatoscopy is one of the core competencies of dermatological training and daily practice. The aim of the study was to establish a structured dermatoscopy curriculum during residency at the University Hospital Augsburg. An online platform with dermatoscopy modules was created, accessible regardless of time and place. Practical skills were acquired under the personal guidance of a dermatoscopy expert. Participants were tested on their level of knowledge before and after completing the modules. Test scores on management decisions and correct dermatoscopic diagnosis were analyzed. Results of 28 participants showed improvements in management decisions from pre- to posttest (74.0% vs. 89.4%) and in dermatoscopic accuracy (65.0% vs. 85.6%). Pre- vs. posttest differences in test score (7.05/10 vs. 8.94/10 points) and correct diagnosis were significant (p<0.001). The dermatoscopy curriculum increases the number of correct management decisions and dermatoscopy diagnoses. This will result in more skin cancers being detected, and fewer benign lesions being excised. The curriculum can be offered to other dermatology training centers and medical professionals.

A childhood secondary headache case associated with Langerhans cell histiocytosis

Neurological involvement is very rare in patients with a diagnosis of Langerhans Cell Histiocytosis (LCH). During the course of the disease, headache can also be seen in addition to systemic effects. A 6 years old girl was admitted to the hospital with only a headache without any systemic symptoms and was diagnosed with LCH. Magnetic resonance imaging showed a solid mass extending from the sphenoid bone corpus to the left great wing of the sphenoid, left orbital apex, left posterior ethmoid cells, left cavernous sinus, and dural space. The correct decision for neuroimaging in patients with headaches is very important in terms of mortality and morbidity. We wanted to share our patient’s case who was diagnosed with LCH by presenting only with headache without any other systemic finding, to remind that LCH may be a secondary cause of headache.

A Literature Study of Big Data Analytics in Marketing

Marketing competition is fierce in today’s business environment. In order for a company to succeed in the market, it is necessary to find valuable information in the market and make a correct business decision. On the other hand, business decisions are usually carried out by management after research, but this step requires accurate data information as a support, and the decision-making plan will be more effective only with the support of effective information. However, big data can be used to conduct scientific analysis of the market data, as well as a comprehensive analysis of the enterprise. So big data analytics is the most critical part of marketing nowadays. This study aims to review the significant and recent research about the applications of big data analytics in marketing and to provide insights to practitioners the necessary analytics in marketing.

Visualization of real-time displacement time history superimposed with dynamic experiments using wireless smart sensors and augmented reality

Wireless smart sensors (WSS) process field data and inform inspectors about the infrastructure health and safety. In bridge engineering, inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements. Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time. Displacement data from WSS in the field undergoes additional processing and is seen at a different location. If inspectors were able to see structural displacements in real-time at the locations of interest, they could conduct additional observations, creating a new, information-based, decision-making reality in the field. This paper develops a new, human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality (AR). It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments. The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser. Using this new AR interface tool, inspectors can observe and compare displacement data, share it across space and time, visualize displacements in time history, and understand structural deflection more accurately through a displacement time history visualization.

A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population

The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.

Comparison of surgical resection of Axillary Lymph Nodes in Dogs with Mammary Gland Tumors with or without sentinel lymph node visualization with patent blue dye.

Dogs' axillary lymph node (ALN) is often difficult to locate before surgical resection. The anatomical location of ALN often discourages Veterinarians from surgical lymphadenectomy. Considering the limited literature available, the actual incidence of metastases and the prognostic relevance are poorly understood. A non-randomized, prospective clinical study was conducted with female dogs (n = 41) with mammary gland tumor (MGT) in the thoracic or cranial abdominal mammary glands. The study investigated the risks of ALN metastasis based on tumors clinical findings, tumor size, histopathological diagnosis and grade. The main aim of this study was to compare ALN resection with or without patent blue 2.5% (PB) dye injection for sentinel lymph node visualization. A total of 46 mastectomies were performed and five animals underwent two mastectomies. In the first group, 17 patients underwent a mastectomy and lymphadenectomy without PB injection (G1). In contrast, in the second group, 24 patients also received PB injections for sentinel lymph node mapping (G2). The ALN was identified in 38/46 cases (82%). The ALN was identified and excised in only 58% of surgeries in G1(19/46), while in group 2, the lymph node was identified in 92% of the cases and resected in 100% of the cases. The use of PB improves ALN's identification and reduces the surgical resection time in dogs with MGT. Surgical time differed between the two groups, as it was significantly shorter in the PB injection group compared to group 1 (80 vs. 45 min) (p < 0.0001). The overall frequency of ALN metastasis was 32%. Macroscopic abnormalities in the lymph nodes, tumor size (>3 cm), and diagnosis of anaplastic carcinoma or grade II/III mammary gland tumors were associated with a higher probability of ALN metastasis. Metastases in the ALNs are more common, in dogs presenting with tumors larger than 3 cm and diagnosed with aggressive histological subtypes. The ALNs should be removed for correct staging, prognostic evaluation, and decision for adjuvant therapy.

An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment

Abstract Practical manufacturing system operates in highly dynamic and uncertain environments, where stochastic disturbances disrupt the execution of the production schedule as originally developed. Previous dynamic scheduling mainly focuses on the constructing predictive models for machine unavailability, with little studies on the adaptive and self-learning capacities for changing scheduling environments. Therefore, a digital twin (DT) driven scheduling with a dynamic feedback mechanism is proposed, in which a reinforcement learning (RL) based adaptive scheduling is developed in DT to make corrective decisions for the disturbances during production runs. In the proposed architecture, the happening disturbance is first detected in the virtual layer by the status continuously updating in accordance with the physical workshop. Furthermore, the reschedule triggering condition is determined in real-time through the calculation of the progress deviations resulting from disturbances. For the scheduling approach, the distributed RL (DRL) based adaptive scheduling method is built to perceive the dynamic production status from virtual environment and implement corrective strategies to hedge against the occurred disturbances. Finally, the proposed method is verified by a practical job shop case and the corresponding DT system is developed to show the effectiveness and advantages after a practical implementation.

Validity of forensic cartridge-case comparisons

This article presents key findings from a research project that evaluated the validity and probative value of cartridge-case comparisons under field-based conditions. Decisions provided by 228 trained firearm examiners across the US showed that forensic cartridge-case comparison is characterized by low error rates. However, inconclusive decisions constituted over one-fifth of all decisions rendered, complicating evaluation of the technique's ability to yield unambiguously correct decisions. Specifically, restricting evaluation to only the conclusive decisions of identification and elimination yielded true-positive and true-negative rates exceeding 99%, but incorporating inconclusives caused these values to drop to 93.4% and 63.5%, respectively. The asymmetric effect on the two rates occurred because inconclusive decisions were rendered six times more frequently for different-source than same-source comparisons. Considering probative value, which is a decision's usefulness for determining a comparison's ground-truth state, conclusive decisions predicted their corresponding ground-truth states with near perfection. Likelihood ratios (LRs) further showed that conclusive decisions greatly increase the odds of a comparison's ground-truth state matching the ground-truth state asserted by the decision. Inconclusive decisions also possessed probative value, predicting different-source status and having a LR indicating that they increase the odds of different-source status. The study also manipulated comparison difficulty by using two firearm models that produce dissimilar cartridge-case markings. The model chosen for being more difficult received more inconclusive decisions for same-source comparisons, resulting in a lower true-positive rate compared to the less difficult model. Relatedly, inconclusive decisions for the less difficult model exhibited more probative value, being more strongly predictive of different-source status.

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the “Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models’ outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

Dynamic decision and its complex dynamics analysis of low-carbon supply chain considering risk-aversion under carbon tax policy

This study establishes a low-carbon supply chain game model under the centralized decision situation and the decentralized decision situation considering the manufacturer risk-aversion behavior, and discusses the influence of the manufacturer risk-aversion behavior on the optimal decision, profit, coordination, and complex dynamics of the supply chain. We find that comparing with the risk-neutral decentralized decision, the increase of manufacturer’s risk tolerance attitude can narrow the gap between the supply chain profit and the centralized decision, but it will further reduce the carbon emission reduction level. The increase of risk tolerance of the manufacturer and carbon tax will narrow the stable region of the system. Under this situation, the manufacturer should carefully adjust parameters to prevent the system from losing stability, especially the adjustment parameters for carbon emission reduction level. When the system is in a chaotic state, the increase of carbon tax rate makes the system show more complex dynamic characteristics. Under the chaotic state, it is difficult for the manufacturer to make correct price decision and carbon emission reduction strategy for the next period, which damages its profit, but increases the profit of the retailer and the supply chain. Finally, the carbon emission reduction cost-sharing contract is proposed to improve the carbon emission reduction level and the supply chain efficiency, achieving Pareto improvement. The stability region of the system is larger than that in the centralized decision situation, but the increase of the cost sharing coefficient will reduce the stability of the system in the decentralized decision-making situation.

Biogenic Solution Map Based on the Definition of the Metabolic Correlation Distance between 4-Dimensional Fingerprints.

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".

Simulated automated facial recognition systems as decision-aids in forensic face matching tasks.

Automated Facial Recognition Systems (AFRS) are used by governments, law enforcement agencies, and private businesses to verify the identity of individuals. Although previous research has compared the performance of AFRS and humans on tasks of one-to-one face matching, little is known about how effectively human operators can use these AFRS as decision-aids. Our aim was to investigate how the prior decision from an AFRS affects human performance on a face matching task, and to establish whether human oversight of AFRS decisions can lead to collaborative performance gains for the human-algorithm team. The identification decisions from our simulated AFRS were informed by the performance of a real, state-of-the-art, Deep Convolutional Neural Network (DCNN) AFRS on the same task. Across five pre-registered experiments, human operators used the decisions from highly accurate AFRS (> 90%) to improve their own face matching performance compared with baseline (sensitivity gain: Cohen's d = 0.71-1.28; overall accuracy gain: d = 0.73-1.46). Yet, despite this improvement, AFRS-aided human performance consistently failed to reach the level that the AFRS achieved alone. Even when the AFRS erred only on the face pairs with the highest human accuracy (> 89%), participants often failed to correct the system's errors, while also overruling many correct decisions, raising questions about the conditions under which human oversight might enhance AFRS operation. Overall, these data demonstrate that the human operator is a limiting factor in this simple model of human-AFRS teaming. These findings have implications for the "human-in-the-loop" approach to AFRS oversight in forensic face matching scenarios. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

THE DEVELOPMENT AND ANALYSIS OF ECO-PRINT AND SCREEN PRINTING COMBINATION USING NATURAL DYES

Natural dye is one of the solutions to alleviate environmental damage caused by the textile industry. Existing synthetic dyes have been proven to adversely affect the environment and human. Hence, revisiting natural dyes becomes a correct decision to alleviate existing environmental problems. Eco-print is one of the natural dyeing methods done by using plants typically found in the surroundings. This method has become the subject of various developments, both in its mordanting and making processes. Its visual aesthetics should also receive attention to avoid stagnation. Further development and innovation are necessary. The purpose of this study was to develop the visual aesthetic aspect of the eco-print method. It particularly focused on developing the design and production technique in terms of concepts, materials, and working techniques. This work also adopted a screen printing technique to enrich the motifs and colors. The method utilized in this research was art-based research with the premise of adapting artistic creativity to social research using an artistic practice approach because both are holistic and dynamic. The result showed that eco-print and screen printing methods could be combined, as the latter significantly enhance the eco-print product’s visual aesthetics. However, the result of this combination exhibited poor color fastness, implying the need for further study.

İnsidental Adrenal Kitlelerin Değerlendirilmesinde Manyetik Rezonans ve Bilgisayarlı Tomografinin Direkt ve Dolaylı Maliyetlerinin Karşılaştırılması

Objective: It was planned to compare the direct and indirect costs of computed tomography (CT) and magnetic resonance (MR) methods in the differentiation of adrenal adenoma and non adenoma.&#x0D; &#x0D; Material and Methods: CT, MR, pathology, laboratory and other information of the patients were obtained from the patient files. Radiological examination and unilateral laparoscopic adrenalectomy cost information was obtained from the accounting department. For cost calculation; the scenario was used in which 500 patients were referred equally to CT and MR, individuals diagnosed with non-adenoma were referred to the operation after the first control, individuals diagnosed with adenoma were followed, and standard follow-up was done every 6 months for 4 years.&#x0D; &#x0D; Results: A total of 31 patients (8 males and 23 females) were eligible for the study. The mean age of the patients was 48.1+17.7 years. In the CT group, an excess cost of 609,0 ₺ per person for 1 year and 2435,9 ₺ per person for 4 years was incurred. In the diagnosis of adenoma and non-adenoma, CT was inaccurate in 24.4% of patients, whereas MR was inaccurate in 5.2%. When used for the diagnosis of pheochromocytoma, it was found that in the CT group, 609,0 ₺ per person for 1 year and 2435,9 ₺ per person for 4 years resulted in an excess cost, whereas CT was erroneous in 17.6% of patients and MR in 15.8% of patients. In the diagnosis of malignancy, in the CT group, an excess cost of 522,2 ₺ per person for 1 year and 2088,7 ₺ per person for 4 years was incurred. It was determined that correct decision could not be made in 33.2% of the patients in the CT group and in 5.8% of the patients in the MR group.&#x0D; &#x0D; Conclusion: The use of contrast-enhanced MR in patients of adrenal masses reduces unnecessary interventions and the resulting expenditures.

Autopsi Forensik Sebagai Upaya Mencapai Kepastian Hukum Pada Kasus Kematian Tidak Wajar

Autopsy is a medical examination of a corpse to determine the cause of death. Forensic autopsies for judicial purposes have been regulated in Article 133 and Article 134 of the Code of Criminal Procedure, even Article 222 of the Criminal Code has also regulated criminal sanctions against parties who intentionally hinder forensic autopsies, but it is undeniable that there are still obstacles in its implementation. This study aims to analyze the essential role of forensic autopsies for legal certainty through analysis of several court decisions and literature review using normative juridical methods with statutory and conceptual approaches. Forensic autopsies has scientific validity. Application of the principle of causality is very important in law enforcement for a fair and correct decision so that it can fulfill a sense of justice for victims, perpetrators and also the community. The accurate cause of death through a forensic autopsy, is a material justification that can be verified scientifically for discovering the truth.