DESCRIPTIONS:
Fog computing has emerged as a proactive solution for healthcare service in recent
years. This is due to the fact that it enables continuous monitoring of the health of
remote patients and early diagnosis of diseases that are carried by mosquitoes. In
addition to this, fog computing lessens the latency and communication cost, both of
which are often significant concerns with cloud computing. The primary purpose of the
intelligent system that has been proposed is to diagnose and stop the spread of
diseases that are transmitted by mosquitoes at an early stage. In order to accomplish
this objective, wearable and Internet of Things (IoT) sensors are utilised to collect the
necessary information, and fog computing is utilised to analyse, categorise, and
facilitate the sharing of medical information between the user and healthcare service
providers. We use the similarity coefficient to discriminate between the various
mosquito-borne diseases based on the symptoms exhibited by the patient, and we
make use of the fuzzy k-nearest neighbour approach in order to classify the user as
either sick or uninfected. In addition, the spread of mosquito-borne diseases is modelled
using Social Network Analysis (SNA), which is done on the cloud layer. By computing
the PDO, or "Probability of Disease Outbreak," one may determine the possibility that a
registered user would contract or spread the disease. This information is then utilised to
deliver location-based awareness in an effort to prevent the disease from spreading.
The experimental evaluation shows that the proposed F-HMRAS has increased
performance, with a classification accuracy of 95.9 percent. Malaria is an infectious
disease that can be fatal to people, despite the fact that it is both contagious and
preventable. There were 228 million instances of malaria reported worldwide in 2018,

with 93 percent of patients and 94 percent of deaths occurring in Africa within Burundi.
This is a significant increase over previous years (51 percent ). A design for an Internet
of Things-based intelligent monitoring and alarm system for malaria patients in Burundi
is proposed in this paper. The patients will utilise a body temperature sensor once they
have been found to have a positive test result while they are at home taking the
medicines that were recommended to them by the doctor. If the patient's condition
continues to deteriorate, they will need to be admitted to the hospital so that a sensor
that measures the concentration of quinine in the serum can be used. The Arduino
Microcontroller will be connected to a body temperature sensor and a water level
sensor. Once this is done, all of the information will be collected and then uploaded to
the IoT server through the internet. The structure of the system is genuine and effective
in meeting the requirements for lowering mortality rates in Burundi. In addition, the
system is designed to send a warning message to the personnel in the medical facility
in the event that the patient's conditions become critical. It is possible to draw the
conclusion that a Smart monitoring and alert system is required in order to monitor
patients who have malaria and can issue an alarm in the event that a serious condition
develops. The patients suffering from malaria who are being treated in Room A of the
Burundi hospital are the focus of this research proposal. After a positive test for malaria
was conducted on this patient, observation of the patient began. The information that is
generated from the patient will be sent to the IoT server via a body temperature sensor
that will be attached to the patient. Patients will be admitted to the hospital; rather than
feeling better after taking their medication as prescribed, they will continue to
experience vomiting and other symptoms. During their stay in the hospital, patients will
make use of two sensors, namely body temperature sensors for the purpose of
monitoring the temperature and water level sensors in order to manufacture serum that
will be injected into the patient. This technology is able to generate an alert notification

in the event that a medical team is required to assist the patient in the event that the
serum has run out or in the event that the patient's temperature has increased or
decreased. Before the system was developed, the patient's guard was the one who
would contact the medical team in the event of an emergency. This would happen, for
example, when the patient's temperature changed drastically or the serum supply ran
out. But from this point on, everything will be analysed using data collected from a safe
distance. It is possible to draw the conclusion that intelligent monitoring and emergency
alert systems are necessary for the success of the system. These systems are used to
monitor patients who have malaria and can sound an alarm in the event that the patient
develops severe symptoms that require immediate medical attention.

WE CLAIMS

1. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms a cutting-edge science technology.

2. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that it can be used for a variety of
purposes, including early disease detection, diagnosis, and treatment.

3. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said the proposed system is more
accurate and faster.

4. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that in this paper, we analyzed and
discussed various aspects.

5. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that in recent years, Dengue, Malaria
diseases detection become a hot topic in medical system.

6. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and

Deep Learning Algorithms of claim 1, wherein said that it is a reliable and efficient
system for monitoring variables.

7. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that this research looks at all of the
important and recent work that has been done so far, as well as its limitations and
challenges.

8. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that Additional disease types may be
studied in the future.

9. Artificial Intelligence and IoT based Automatic smart Healthcare system to Prevention
and Detection of Dengue, Malaria and other types of viral fevers using Data mining and
Deep Learning Algorithms of claim 1, wherein said that a future study could compare
the performance of various machine learning algorithms used in the diagnosis of Cancer
diseases.