CardioAI

AI-powered mobile app using Random Forest algorithm for early detection of cardiovascular disease

The specific problem that my solution aims to solve is the late detection of cardiovascular disease (CVD). CVD is the leading cause of death worldwide, and often passes asymptomatically, which is the main problem of its late detection. This results in a significant burden on individuals, families and communities, as well as on healthcare systems globally. CVD is responsible for 17.9 million deaths per year, and this number is projected to increase to 23.6 million by 2030. The World Health Organization (WHO) estimates that 80% of premature CVD deaths can be prevented.

The scale of the problem in my community, specifically in Kazakhstan, is significant as well. According to the Ministry of Health of the Republic of Kazakhstan, cardiovascular diseases are the leading cause of death in the country. In 2018, the number of deaths from CVDs was 38,958, which is 54% of all deaths in the country. Furthermore, the number of people affected by CVDs is also high, as according to the Ministry of Health, in 2018, there were 767,811 registered cases of CVDs in the country. One specific example is the case of 18 year old Maxim Korolev from Astana, who died during a half marathon in Almaty in 2018 due to sudden cardiac death. This is a tragic reminder of the importance of early detection of CVD, as young and healthy individuals may be at risk of the disease without knowing it. 

The factors contributing to the problem in my community include a lack of awareness and education about CVD, unhealthy lifestyle choices such as smoking, unhealthy diet, and lack of physical activity, as well as a lack of access to early detection and treatment. These factors contribute to the high rates of CVD in Kazakhstan, where access to healthcare and education is often limited for particular groups of people.

The consequences of late detection of CVD in Kazakhstan include increased morbidity and mortality, as well as increased healthcare costs. CVD can lead to disability and death, as well as a significant burden on individuals, families, and communities. Late detection of CVD also results in increased healthcare costs, as the disease is more difficult and expensive to treat in its later stages.

In conclusion, the problem of late detection of CVD is a significant global health issue that affects millions of people worldwide, with a disproportionate impact on low and middle-income countries, including Kazakhstan. The proposed solution aims to address this problem by using AI and ML to develop a mobile application that can accurately detect CVD in its early stages, making it accessible and available for anyone to use, thereby protecting themselves from the risks of exacerbation of CVD, disability and death.

My solution is an Artificial Intelligence model that uses a Machine Learning (ML) algorithm called Random Forest (RF) for the early detection of Cardiovascular Disease (CVD). The model is designed to classify benign and malignant CVD lesions, using a dataset of 1000 data samples. The model takes as input 13 parameters related to a patient's health, such as age, sex, chest pain type, and resting blood pressure. The model then uses this information to predict whether the patient has CVD or not.

The model is trained using a supervised learning approach, where it is fed with labeled data and it learns to identify patterns and relationships between the input parameters and the output labels. In this case, the input parameters are the patient's health data, and the output label is whether the patient has CVD or not (0 or 1).

The Random Forest algorithm is a supervised learning algorithm that is used for classification and regression problems. It creates an ensemble of decision trees, where each tree is built using a random subset of the data. The output of the model is the majority vote of all the trees in the forest. This makes the model more robust and less prone to overfitting. 

The model was trained on a heart disease dataset consisting of over 1000 data samples, and it achieved an accuracy of 93.17%. The solution also includes plans for further development such as the integration of the created model into a mobile application that anyone can use. The mobile application would have a user-friendly interface that allows users to input their health data and know whether they belong to the risk group for CVD. Moreover, it will have useful features such as finding the closest doctor in the city and providing tips for minimizing the risk of CVD.

Currently, many people who are at risk of CVD may not be aware of their condition, as the disease often passes asymptomatically. This is particularly true for active individuals who engage in marathon running, as they may not realize that they have an underlying health condition that puts them at risk of sudden cardiac death.

Therefore, my solution serves people who are at risk of developing cardiovascular disease or who have undiagnosed CVD. The target population of this solution includes individuals who may have one or more risk factors for CVD, such as high blood pressure, high cholesterol, diabetes, smoking, or a family history of CVD, as well as people who are active and engage in marathon running. These individuals may be currently underserved as they may not have access to early detection and treatment due to lack of awareness, education, or access to healthcare.

The solution addresses the needs of this population by providing them with a convenient, accessible, and accurate way to detect CVD in its early stages. The mobile application will allow users to input their health data and receive a prediction of whether they have CVD or not, which will only take 0.02 seconds to output the result. This will enable them to take steps to address their condition, such as starting treatment, seeking further medical evaluation, or making lifestyle changes. This tremendously saves time when waiting for an appointment with a cardiologist, which is a common problem in Kazakhstan, especially in hospitals of megapolises such as Almaty.

The application utilizes an Artificial Intelligence model based on a machine learning algorithm to accurately classify benign and malignant CVD lesions. The application takes 13 parameters as input data, including age, sex, chest pain type, and various measures of heart rate and blood pressure.

The most important impact this solution will have is saving thousands or millions of lives of those, who were unaware of their diagnosis, which as a result will allow them to start the treatment early and survive from this asymptomatic disease. Nonetheless, not only would it impact the people under the risk of CVD, but it will also minimize the workload on doctors, who will benefit due to the lack of available doctors. By providing this application, the solution aims to empower individuals to take control of their health, and reduce the risk of CVD-related complications and deaths, especially for those who tend to do lots of physical activity like marathon runners.

As a student with a background in computer science, mathematics, and business management, I am well-positioned to deliver this solution. My academic background has given me a strong understanding of the potential of technology to solve real-world problems, and the skills necessary to develop and implement effective solutions. My experience in AI and ML, as well as my knowledge of healthcare, have prepared me to develop an accurate and efficient model for detecting CVD.

My past experiences in developing mobile apps for people with dementia, and fighting violence and harassment in taxis, have provided me with the skills to design user-centered solutions and understand the challenges faced by those in need. My involvement in the startup DermaMarker, and my experience in the application of AI in healthcare, have further solidified my understanding of the potential of technology to improve healthcare.

Additionally, my personal connection to the healthcare industry through my parents being doctors, has given me a unique perspective and understanding of the challenges faced by those with CVD, and a desire to use my skills to improve healthcare. Furthermore, my participation in the Republican Competition of Scientific Projects 2022, where I took 2nd place in the section of applied mathematics, has further solidified my understanding of the potential of AI and ML in healthcare.

Overall, my academic background, experience in AI, and understanding of healthcare, combined with my past experience in developing mobile apps, and my personal connection to the healthcare industry, make me well-positioned to deliver this solution and improve the lives of those at risk of developing CVD.

I have taken several steps to understand the needs of the population I want to serve. Firstly, I have done research with potential users, by conducting interviews and surveys with people who are at risk of developing CVD or who have undiagnosed CVD. This helped me to understand their experiences, challenges, and needs. Secondly, I have engaged potential users in the design and development of my solution by showing them the AI model, and receiving feedback and suggestions on how to improve the model and what to include in the mobile application. Additionally, I have worked with a cardiologist at the Interteach international clinic, who has tested the program I proposed on her work, and provided feedback about its usability and potential for further use in medical clinics. Furthermore, I have also considered the 17 United Nations Sustainable Development Goals and how my solution addresses one or more of them.

My solution is innovative because it utilizes an Artificial Intelligence model based on a machine learning algorithm, Random Forest, to accurately classify benign and malignant CVD lesions. This approach is unique compared to traditional methods of CVD detection, which often rely on manual analysis and interpretation of patient data by doctors. Additionally, my solution is designed for use by the general public, making it accessible and easy to use for anyone, thus increasing the chances of early detection of CVD.

The use of AI and ML to detect CVD has the potential to be catalytic in the healthcare industry, as it can change the market by making early detection more efficient and accessible to a wider range of individuals. This can enable broader positive impacts from others in this space, as it could lead to more people being aware of their CVD status, and taking steps to address it earlier on. Additionally, by using a mobile application, it makes it more accessible to people in remote areas or with limited access to healthcare.

In terms of impact, my solution has the potential to change the market by providing a more efficient and accurate method of detecting CVD, which could lead to early treatment and better outcomes for patients. Additionally, by making the technology accessible to the general public, it could enable broader positive impacts from others in the healthcare space by increasing awareness and education about CVD prevention.

1. Developing the mobile app: In order to achieve this goal, we will need to complete the coding and programming of the AI model and integrate it into a user-friendly mobile application. This will involve testing and refining the model to ensure its accuracy and reliability, as well as designing the user interface and incorporating any additional features such as finding the closest doctor in the city and providing tips for minimizing the risk of CVD.

2. Conducting beta testing to a certain number of users: To achieve this goal, we will need to identify a representative sample of our target population and recruit them to test the application. This will involve reaching out to potential users through different channels such as social media, online forums, and community groups. We will also need to establish a system for collecting and analyzing feedback from beta testers to identify any issues or areas for improvement.

3. Reaching a user base of 1,000 individuals: We will achieve this goal by actively promoting the application through various channels such as social media, online advertising, and partnerships with relevant organizations. We will also look to collaborate with healthcare providers and other relevant stakeholders to increase awareness of the application and its benefits.

4. Continuously updating the dataset on which the model is trained to ensure its accuracy: To achieve this goal, we will need to continue testing and refining the AI model to ensure that it is accurately identifying CVD in the majority of cases. This will involve collecting and analyzing large amounts of data to identify patterns and trends, as well as working closely with healthcare professionals to validate the model's predictions.

5. Providing a cost-effective solution: To achieve this goal, we will need to keep the cost of the application as low as possible while still ensuring its quality and effectiveness. This will involve making use of open-source technologies and minimizing the use of expensive proprietary software. Additionally, we will look for ways to monetize the application without increasing costs for users, such as through advertising or partnerships.

6. Reducing the rate of late-stage CVD diagnoses: To achieve this goal, we will need to ensure that the application is easily accessible and user-friendly, and that it is accurately identifying CVD in its early stages. We will also need to work closely with healthcare providers to ensure that users are being directed to appropriate medical care in a timely manner.

7. Improving access to healthcare for underserved communities: To achieve this goal, we will need to ensure that the application is accessible to a wide range of users, including those in underserved communities. This will involve working closely with community organizations and healthcare providers to identify and address any barriers to access, such as lack of internet connectivity or language barriers.

The core technology that powers my solution is a machine learning algorithm called Random Forest (RF). RF is an ensemble learning method that combines multiple decision trees to make predictions. In the context of this classification task, RF is used to classify benign and malignant CVD lesions by analyzing a large dataset of patient health data. The RF algorithm creates multiple decision trees, each of which is trained on a different subset of the data. The final prediction is made by taking a majority vote from all the decision trees in the ensemble. The algorithm also considers the importance of each feature when making predictions, which helps to identify the most important factors in determining CVD. The use of multiple decision trees in RF allows for greater accuracy and robustness in predictions, as well as the ability to handle large and complex datasets. Furthermore, the RF algorithm is able to handle the overfitting problem of the decision tree. It reduces the variance and increases the bias of the model by averaging the predictions of multiple decision trees. Additionally, by using a random subset of features at each split, it also reduces the correlation between decision trees, further improving the model's performance. Overall, RF is an ideal algorithm for this classification task as it can handle high-dimensional data and improve the accuracy of predictions.

Currently, my solution does not serve any people as it is still in the development stage. However, in the next year, I plan to serve at least 1000 people in the initial stage by providing them with an easy and accessible way to detect CVD in its early stages through my mobile application. This number is expected to grow because the mobile app is addressing a significant need in the community for early detection and prevention of CVD. The app will be easily accessible to anyone with a smartphone, which is a widely available technology in Kazakhstan. Additionally, the app utilizes a machine learning algorithm, Random Forest (RF), which has been shown to be highly accurate in classifying benign and malignant CVD lesions. This will give users a high level of confidence in the app's ability to detect CVD. Furthermore, the app will have additional features such as finding the closest doctor in the city and providing tips for minimizing the risk of CVD. This will provide a valuable service for users, which will encourage them to use the app regularly. Additionally, as the application is planned to be offered for free, it is expected to be easily accessible for a large population and will be promoted by various organizations that conduct marathons, which will greatly contribute to the number of users.

There are several barriers that currently exist for me to accomplish my goals in the next year:

Financial: Developing and launching a mobile app requires a certain amount of financial resources, such as the costs of hiring a developer, designer, and marketer. Additionally, beta testing and gathering feedback from potential users can also be costly.

Legal: Compliance with data protection and privacy laws can be challenging. Additionally, obtaining the necessary approvals and certifications from relevant authorities can also be time-consuming and costly.

Cultural: The concept of using AI and machine learning in healthcare may not be well understood or accepted by some part of the general population.

Market: The market for CVD detection and prevention solutions in Kazakhstan may be relatively small and not well developed, making it difficult to attract investors or partners.