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Dengue;Autonomic computing; Clinical decisionsupport system; Computational intelligence

Introduction

Dengue is AN arthropod-borne infectious agent illness transmitted by Aedes mosquitoes, chiefly Aedes aegypti and Aedes albopictus [1]. Currently, this infection is taken into account the foremost necessary arbovirosis worldwide in terms of morbidity, mortality and economic impact [2]. Between medicine weeks one and forty nine of 2021, 1,173,674 dengue fever cases within the Americas region were rumored, with a accumulative incidence rate of 118 cases per a hundred,000 inhabitants during this amount, the foremost affected subregions were the Southern Cone with a accumulative incidence of 323 cases/100,000 inhabitants, and also the range subregion with eighty nine cases/100,000 inhabitants. at intervals the range subregion, South American country is in third place with AN incidence of ninety five cases per a hundred,000 inhabitants, surpassed by South American country and Republic of Ecuador with one hundred forty and 108 cases per a hundred,000 inhabitants, severally [3]. Mortality rates for dengue fever will be high once identification and treatment aren't applicable, reaching values of 2 hundredth [4].

With reference to the antecedently bestowed background, the contribution of this paper may be a clinical DSS exploitation AN autonomous cycle of information analysis tasks (ACODAT) to assist decision-making in clinical settings. specifically, ACODAT uses the interaction of various ordered tasks to extract the required data to advocate enhancements in a very given method [5]. the employment of ACODAT in several fields like education, telecommunications and business four.0, are rumored as an example, within the instructional field, ACODAT has been accustomed confirm learning designs in good school rooms [6]. used ACODAT to investigate net and social network information to make data models concerning students. These models area unit accustomed for good monitor the educational method. The results showed the capability of ACODAT for the generation of helpful data to enhance the educational method within the field of telecommunications [7] developed ACODAT for quality of service management in web of Things (IoT) platforms. The enforced ACODAT allowed analyzing the standard of IoT platforms exploitation classification and agglomeration tasks. In business four.0, ACODAT has been developed and enforced to enhance the potency of production processes [8,9]. bestowed a framework that helps to resolve the issues of integration and nonuniformity of the actors concerned in producing processes. The results show that ACODAT allowed to those actors (people, data, things and services) to act for the creation of a self-configuration and self-optimization set up. Finally, it additionally has been utilized in good cities, to manage and supervise heating, ventilation, and air con systems [10].

The remainder of this paper is structured as follows: Section two presents a short literature review concerning dengue fever modeling for the clinical management of dengue fever. Section three introduces the generalities of dengue fever and also the conceptualization of ACODAT. Section four describes the ACODAT projected during this article, and also the methodology used for its definition and implementation. Section five shows the results of ACODAT's implementation in 2 dengue fever datasets. Section half-dozen discusses the results and compares them with previous studies. Finally, Section seven concludes the paper.

Early detection of dengue fever

Early detection of {dengue|dengue fever|dandy fever|breakbone fever|infectious illness} is troublesome and difficult thanks to the dearth of specificity within the clinical presentation of the disease. However, in recent years, computer-aided methods are developed to support medical professionals in these troublesome tasks [11].as an example used 2 techniques, supplying regression (LR) and call trees (DT), to develop prognostic models for the assessment of doable early dengue fever infections. The authors used self-reported clinical manifestations from patients in non-endemic regions. the simplest performance was from the DT model with a neighborhood below the curve (AUC) of zero. compared many mil techniques to spot confirmed dengue fever cases exploitation solely age, vital sign, white corpuscle count and protoplasm count. Models were designed with deep learning, DT and LR, wherever deep learning performed best with AN United Self- Defense Group of Colombia of zero [12].

Developed a DSS for dengue fever exploitation fuzzy psychological feature maps (FCM). They enforced diagnostic models exploitation FCM to classify patients in keeping with the sort of dengue fever, with AN accuracy of zero.89. Also, they analyzed the behavior of signs, symptoms, laboratory tests and illness severity. This study goes more, and not solely classifies the patient, however additionally evaluates the behavior of the signs and symptoms of dengue fever over time, giving recommendations on what factors would possibly influence and seem within the course of the illness

Dengue fever treatment

Treatment of dengue fever consists of palliating symptoms and avoiding complications resulting in death. The complexness of the treatment is portrayed by the high variability of the clinical manifestations bestowed. Despite United Nations agency recommendations, the treatment of dengue fever remains a challenge for medical professionals. Sadly, to date, no process models are developed to support higher cognitive process relating to the treatment of dengue fever.

In summary, the approaches projected for the identification of dengue fever supported severity area unit few. The models developed by and have the limitation of solely detection the illness while not classifying it. On the opposite hand, the approaches developed for the classification of dengue fever have limitations like the low classification performance within the work of , or the employment of genetic information by [13-15], that isn't helpful in clinical apply as a result of this kind of information isn't simple offered for the practitioner. Finally, there aren't any prescribing approaches that advocate treatment choices for dengue fever.

Theoretical background

Clinical management of dengue fever

In this section, we have a tendency to describe the principal aspects of dengue fever, as well as generalities, identification and proposals for treatment.

Identification of dengue fever

The definitive and confirming identification of dengue fever is created exploitation direct strategies like virus isolation, detection of infectious agent super molecule or antigens; and indirect strategies like detection of antibodies made against the virus. However, these laboratory tests will take an extended time, that may cause the patient with dengue fever to develop complications and die. to resolve this drawback, there area unit dengue fever identification pointers printed by United Nations agency . These pointers state that the primary step within the identification of dengue fever is that the general analysis of the patient by the doctor to classify the patient into a group: NoWSDengue, YesWS-Dengue & American state. The physical examination, analysis of the anamnesis, and laboratory tests like a whole blood count, enable the identification of warning signs and analysis of the patient's association standing. Classification of the patient into a gaggle constitutes the second stage within the clinical management of dengue fever. the employment of this guide is crucial to supply adequate management of the illness thanks to the wide spectrum of clinical manifestations of dengue fever.

Recommendations for treatment

The third step within the clinical management of dengue fever is treatment. The data obtained within the previous 2 steps is significant to supply AN adequate and timely treatment for the patient with dengue fever. Table one summarizes the clinical management of dengue fever, by treatment cluster, supported the United Nations agency pointers. The treatment routes for dengue fever area unit categorised into 3 teams (A, B & C). In group A, we've got patients United Nations agency don't gift warning signs or comorbidities and United Nations agency tolerate oral water volumes. additionally, this cluster includes patients with adequate symptom. In group B, we've got patients with warning signs or pre-existing conditions like DM, obesity, nephrosis, pregnancy, among others. Patients with some social conditions, like living alone or living off from a health establishment, are classified during this cluster. Finally, cluster C constitutes all patients with any of the subsequent complications: severe plasma extravasation, severe trauma, shock, and severe organ deterioration.

Conclusion

This paper projected a clinical DSS for dengue fever exploitation ACODAT. The target was to develop a system that enables the process of information, classification of the patient in keeping with the sort of dengue fever, and supported this last characteristic, recommendation of the simplest treatment possibility from an inventory of accessible treatments. The ACODAT developed has the flexibility to arrange the information and method them in order that they're prepared for subsequent task of the cycle. The AI techniques used, ANN and SVM, have the flexibility to properly classify patients with high performance.The GA utilized in the last task of the cycle has the potential to advocate (prescribe) the simplest treatment possibility in keeping with symptoms, signs and laboratory tests. The joint use of information analysis tasks in a very cycle had key blessings over separate approaches. One in every of them is time to diagnose. With the projected approach, it's doable to diagnose and advocate mechanically patient treatment. This can be important as a result of the time to diagnose and treat dengue fever is crucial to avoid complications and death of patients. To the simplest of our data, this can be the primary work that uses AN involuntary approach to support the clinical management of dengue fever. Additionally, it's the primary work to propose a prescriptive model for the clinical management of this illness.

This study has many limitations. First, some variables concerned within the overall assessment method by the medical skilled weren't offered to be enclosed within the implementation of the models. Second, the inaccessibility of cohort datasets (before/after) to verify whether or not the counseled treatment had a positive impact on patients' health. For this latter, it's necessary to validate the results of this study in real hospital environments.

Future work ought to be geared toward raising the models enforced exploitation routine laboratory tests like white corpuscle counts, blood levels of liver enzymes and cytokines. Additionally, the inclusion of comorbidities like polygenic disease and blood vessel cardiovascular disease may improve the performance of the models thanks to the influence of those diseases on the severity of dengue fever. Finally, the creation of accessible datasets with prescriptive or treatment variables would be helpful to validate the results of prescriptive models.

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