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About Us

Releena Research LTD is a Data Science research company dedicated to advancing medical diagnostics and treatment through innovative research in neuroscience and microbiology. Our mission is to improve patient outcomes by developing cutting-edge solutions that address the complex challenges in healthcare. At Releena Research, we believe in collaboration, innovation, and excellence. Our team of scientists, engineers, and data analysts work together to develop novel approaches to diagnosing and treating neurological and microbiological disorders. With a focus on interdisciplinary research and technological innovation, we strive to make a meaningful impact on healthcare and improve the lives of patients around the world.


Collaborative Approach

Our collaborative approach brings together experts from diverse disciplines, including neuroscience, microbiology, computer science, and data analytics. By leveraging the collective expertise of our partners, we are able to develop innovative solutions that push the boundaries of computational thinking and medical research.

Understanding Alzheimer's Disease

One area of focus for our collaborative research efforts is understanding and combating Alzheimer's disease. By working closely with leading universities and medical institutions, we gain access to large-scale medical datasets and cutting-edge research findings. This allows us to conduct in-depth analyses to uncover the underlying mechanisms of Alzheimer's and develop new diagnostic tools and treatment strategies.

Machine Learning Model for Dementia Prediction

We have developed a machine learning model to predict early signs of dementia using the OASIS-2: Longitudinal dataset. The model uses features such as 'M/F', 'MMSE', 'eTIV', 'nWBV', 'ASF', and 'Group' to label patients as demented or non-demented.

Check out the project on GitHub: Dementia Prediction Model.

Understanding Dementia

The age distribution of patients is concentrated between 60 and 100 years, with the most common age group around 75 to 85 years. There are fewer patients above 95 years or below 60 years. The age distribution by group indicates that patients who have transitioned to dementia (Converted) have a median age close to 90, while those diagnosed with dementia (Demented) have a median age around 80. Nondemented patients have similar ages to the Demented group, with a median slightly above 80. Older age is associated with both being diagnosed with dementia and transitioning to dementia. The scatter plot of eTIV (estimated Total Intracranial Volume) versus nWBV (normalized Whole Brain Volume) shows a broad spread of nWBV values for a given eTIV, indicating variability in brain volume relative to total intracranial volume. Most patients cluster around eTIV values between 1250 and 1750, with nWBV values between 0.65 and 0.75, suggesting that brain volume normalization varies widely among individuals. The data highlights the significance of age in dementia and the complex relationship between brain volume measurements and dementia status, emphasizing the need for individualized assessment in diagnosing and managing dementia.

Dementia Analysis eTIV vs. nWBV in Male and Female

Probability by Gender

Data Source: OASIS-2: Longitudinal: Principal Investigators: D. Marcus, R. Buckner, J. Csernansky, J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382.


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