This project provides an insight into an investigation of the classification of breast cancer sub-types using proetomic dataset through a machine learning approach.
This project uses artificial intelligence to explore the possibility of using a facial image analysis to detect Autism in children. Early detection and diagnosis of Autism, along with treatment, is needed to minimize some of the difficulties that people with Autism encounter. Autism is usually diagnosed by a specialist through various Autism screening methods. This can be an expensive and complex process. Many children that display signs of Autism go undiagnosed because there families lack the expenses needed to pay for Autism screening and diagnosing. The development of a potential inexpensive, but accurate way to detect Autism in children is necessary for low-income families. In this project, a Convolutional Neural Network (CNN) is utilized, along with a dataset obtained from Kaggle. This dataset consists of collected images of male and female, autistic and non-autistic children between the ages of two to fourteen years old. These images are used to train and test the CNN model. When one of the images are received by the model and importance is assigned to various features in the image, an output variable (autistic or non-autistic) is received.
Project: Analyzing the Advantages and Disadvantages of Artificial Intelligence for Breast Cancer Detection in Women
Breast Cancer is one of the most dangerous type of disease that affects many women. For detecting Breast Cancer, machine learning techniques are applied to improve the accuracy of diagnosis.
Cervical Cancer is an increasing matter that is affecting various women across the nation, in this project we will be analyzing risk factors that are producing higher chances of this cancer. In order to analyize these risk factors a machine learning technique is implemented to help us understand the leading factors of cervical cancer.
Artificial intelligence is a branch of computer science that focuses on building and programming machines to think like humans and mimic their actions. The proper concept definition of this term cannot be achieved simply by applying a mathematical, engineering, or logical approach but requires an approach that is linked to a deep cognitive scientific inquiry. The use of machine-based learning is constantly evolving the dental and medical field to assist with medical decision making process. In addition to diagnosis of visually confirmed dental caries and impacted teeth, studies applying machine learning based on artificial neural networks to dental treatment through analysis of dental magnetic resonance imaging, computed tomography, and cephalometric radiography are actively underway, and some visible results are emerging at a rapid pace for commercialization. Researchers have found deep convolutional neural networks to have a future place in the dental field when it comes to classification of dental implants using radiographic images.
Analyzing factors as immune systems, genetics and diets than can lead to Hashimoto disease
This work implements machine learning algorithim apply in Multiple Sclerosis symptoms and provides treatment options available
In this effort we are analyzing X-ray images in AI and identifying cavitites
With the ready availability of COVID-19 vaccinations, it is concerning that a suprising large portion of the U.S. population still refuses to recieve one. In order to control the spread of the pandemic and possibly even erradicate it completely, it is integral that the United States vaccinate as much of the population as possible. Not only does this require ensuring that everyone who wishes to be vaccinated recieves a vaccine, it also requires that those who are unwilling to recieve the vaccine are persuaded to take it. The goal of this report is to analyze the demographics of those who are hesitant to recieve the vaccine and find the reasoning behind their decision. This will make it easier to properly persuade them to recieve the vaccine and aid in raising the United States' vaccination rates.
By the end of 2019, healthcare across the world started to see a new type of Flu and they called it Coronavirus or Covid-19. This new type of Flu developed across the world and it appeared there is no one treatment could be used to treat it yet, scientists found different treatments that apply to different age ranges. In this project, We will try to work on comparison analysis between USA and China on number of new cases and new deaths and trying to find factors played big roles in this spread.
Machine learning has been a mainstay in drug discovery for decades. Artificial neural networks have been used in computational approaches to drug discovery since the 1990s. Under traditional approaches, emphasis in drug discovery was placed on understanding chemical molecular fingerprints, in order to predict biological activity. More recently however, deep learning approaches have been adopted instead of computational methods. This paper outlines work conducted in predicting drug molecular activity, using deep learning approaches.
Since our technology is more and more advanced as time goes by, traditional human-computer interaction has become increasingly difficult to meet people’s demands. In this digital era, people need faster and more efficient methods to obtain information and data. Traditional and single input and output devices are not fast and convenient enough, it also requires users to learn their own methods of use, which is extremely inefficient and completely a waste of time. Therefore, artificial intelligence comes out, and its rise has followed the changeover times, and it satisfied people’s needs. At the same time, gesture is one of the most important way for human to deliver information. It is simple, efficient, convenient, and universally acceptable. Therefore, gesture recognition has become an emerging field in intelligent human-computer interaction field, with great potential and future.
Healthcare is an organized provision of medical practices provided to individuals or a community. Over centuries the application of innovative healthcare has been needed increasingly as humans expand their life span and become more aware of better preventative care practices. The application of Big Data within the industry of Healthcare is of the utmost importance in order to quantify the effects of wide scale efficient and safe solutions. Pharmaceutical and Bio Data Research companies can use big data to intake large facets of patient record data and use this collected data to iterate how preventative care can be implemented before diseases actually present themselves in stages that are beyond the point of potential recovery. Data collected in laboratory settings and statistics collected from medical and state institutions of healthcare facilitate time, money, and life saving initiatives as deep learning can in certain instances perform better than the average doctor at detecting malignant cells. Big data within healthcare has proven great results for the advancement and diverse application of informed reasoning towards medical solutions.
As cardiovascular diseases are the number 1 cause of death in the United States, the study of the factors and early detection and treatment could improve quality of life and lifespans. From investigating how the variety of factors related to cardiovascular health relate to a general trend, it has resulted in general guidelines to reduce the risk of experiencing a cardiovascular disease. However, this is a rudimentary way of preventative care that allows for those who do not fall into these risk categories to fall through. By applying machine learning, one could develop a flexible solution to actively monitor, find trends, and flag patients at risk to be treated immediately. Solving not only the risk categories but has the potential to be expanded to annual checkup data revolutionizing health care.
Sports Medicine will be a $7.2 billion dollar industry by 2025. The NBA has a vested interest in predicting performance of players as they return from injury. The authors evaluated datasets available to the public within the 2010 decade to build machine and deep learning models to expect results. The team utilized Gradient Based Regressor, Light GBM, and Keras Deep Learning models. The results showed that the coefficient of determination for the deep learning model was approximately 98.5%. The team recommends future work to predicting individual player performance utilizing the Keras model.
Healthcare is utilizing Big Data to to assist in creating systems that can be used to detect health risks, implement preventative care, and provide an overall better experience for patients. However, there are fundmental issues that exist in the creation and implementation of these systems. Medical algorithms and efforts in precision medicine often neglect the structural inequalities that already exist for minorities accessing healthcare and therefore perpetuate bias in the healthcare industry. The author examines current applications of these concepts, how they are affecting minority communities in the United States, and discusses improvements in order to achieve more equitable care in the industry.
Wearable devices offer an abundant source of data on wearer activity and health metrics. Smartphones and smartwatches have become increasingly ubiquitous, and provide high-quality motion sensor data. This research attempts to classify movement types, including running, walking, sitting, standing, and going up and down stairs, to establish the practicality of sharing this raw data with healthcare workers. It also addresses the existing research regarding the use of wearable data in clinical settings and discusses shortcomings in making this data available.
Chest X-rays reveal many diseases. Early detection of disease often improves the survival chance for Patients. It is one of the important tools for Radiologists to detect and identify underlying health conditions. However, they are two major drawbacks. First, it takes time to analyze a radiograph. Second, Radiologists make errors. Whether it is an error in diagnosis or delay in diagnosis, both outcomes result in a loss of life. With the technological advances in AI, Deep Learning models address these drawbacks. The Deep Learning models analyze the X-rays like a Radiologist and accurately predict much better than the Radiologists. In our project, first, we develop a Deep Learning model and train our model to use the labels for Atelectasis, Cardiomegaly, Consolidation, Edema, and Pleural Effusion that corresponds to 5 different diseases, respectively. Second, we test our model’s performance: how well our model predicts the diseases. Finally, we visualize our model’s performance using the AUC-ROC curve.