Smart Medical Check-Up

The aim is ,to decentralize medical check-ups, using machine learning and the internet of things, in which a patient who is suffering from an abnormal heartbeat can be checked/monitored

The business model is to decentralize medical check-up, that is targeted at detecting normal and abnormal heartbeat rates of patients. The samples of normal and abnormal heartbeats will be obtained from patients and then trained using Matlab, classification learner app, and then deployed on Arduino Nano 33 BLE Sense
board, in which the model can be able to detect an abnormal heartbeat from the normal heartbeat. and then the patient data is then sent to the medical center, through the help of the internet of things and network protocols. If it is abnormal an alarm from the sensors which will be detected from the embedded device will automatically inform the hospital/patient, and as the case may be an ambulance is sent to the patient's location and brought to the medical center, where further treatments will be diagnosed. If normal there will be no need to inform the medical center, but the medical center automatically has the capabilities to monitor the patient status from their systems.

 Specific problem and the scale

Abnormal heartbeat which also known as Arrhythmia has been one of the causes of death rate, according to the National Center for Biotechnology Information(NCBI) it states that "Sudden cardiac death  and arrhythmia represent a major worldwide public health problem, accounting for 15–20 % of all deaths." This has led to the loss of human capital power and reduced productivity of different economies of the world. Hence the problem to be solved is targeted at abnormal heartbeat using proactive and predictive analysis, using machine learning algorithms, and the Internet of Things(IoT).

 How many people are affected?

According to the National Center for Biotechnology Information(NCBI), it states that "Atrial fibrillation is the most frequent cardiac arrhythmia(abnormal heartbeat). It has been estimated that 6-12 million people worldwide will suffer this condition in the US by 2050 and 17.9 million people in Europe by 2060." Hence with this data, this, therefore, deduce the working population of my solution to this problem.

 What factors are contributing to the problem in your community? 

The factors can be  classified into 3 factors 

1. Genetic: which is passed from one generation to another.

2. It can be caused by certain substances or medications intakes, such as caffeine, nicotine, alcohol, cocaine, inhaled aerosols, diet pills, and cough and cold remedies.

3. Emotional states such as shock, fright, or stress can also cause irregular heart rhythms.

Describe the target population whose lives you are working to directly and meaningfully improve

The solution is targeted to the health sector and then narrowed down to patients who are suffering from abnormal heartbeat also known as Arrhythmia. 

Who are they, and in what ways are they currently underserved?

This population group is currently underserved in three ways,

1. Inaccessibility to medical Institutions such as health care: they have limited access to a medical facility to enable them have a checkup

2. Medical Literacy: under this population, most of them do not know they have this ailment 

3. Financial: Inability to go for medical checkups due to the fees incurred.

How will the solution address their needs?

It addresses this targeted population with a decentralized medical approach where this population group can check their heartbeat and this will inform this target group if they have a normal or abnormal heartbeat and then prescribe the best means and solution to them using machine learning algorithm and the internet of things which will be then be communicated back to the hospital patients medical database up for follow up, without necessarily going to a health center, for heartbeat checkups.

hence cutting the hindrance to inaccessibility to medical centers, FInancial hindrance, and improving medical literacy

I have done an in-depth analysis and research of the targeted population group, who are suffering from abnormal heartbeat or arrhythmia. This group of individuals seeks a solution to this problem, and my solution comes in by providing an easy, decentralized approach in proactively informing the user using machine learning algorithms and then informing the medical, establishment with updates about the user heartbeat status, making it easier for the health care center to decipher and provide an immediate solution and also to the user to inform he/she about his medical state, which may infer medical attention before things get worse and unable for the health care worker to mitigate.

I have engaged with potential users about the design and workflow of how it will help them, and you can't believe the smile to have this idea come to existence, which will help them, boost their efficiency, serve as a monitor of their heartbeat which will indicate their heartbeat status if it is normal or abnormal and server as a mobile/decentralized medical center which can be used anywhere in their homes, offices or when traveling, and also help the medical center to help monitor and also keep track of the user heartbeat status, which can be used fo proper follow-up and further diagnosis.

Smart Medical Check-Up is currently at the concept stage, I am currently working on the heartbeat data of both abnormal and normal heartbeats of patients, recording them, in order for us to have the right amount of data in order to train and feed the algorithm which will be able to detect, a normal heartbeat from an abnormal this stage is a very critical and the most crucial stage of Smart Medical Check-Up concept and I believe that with the amount of data acquired from different medical centers, I believe to generate a working model to be able to predict an abnormal and normal heartbeat rate, which will then be deployed on an embedded microcontroller such as Arduino Nano 33 Ble sense, using MATLAB programming software, which is a software which can run design, optimization, visualization of data, and to simulate and control hardware and run different predictive analysis. At this stage, I believe I am currently at the concept stage.

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