shivangi1raghav Goto Github PK
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Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating patients accordingly can help achieve better therapeutic results rather than providing the same treatment for all colorectal cancer patients. Our research aims at identifying molecular subtypes of colorectal cancer using gene expression data by building a model using some machine learning and deep learning algorithms.
Convolutional Neural Networks
Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact etiology of the disease. The exact histology of PCOS is not yet clear. It is therefore a multifaceted study, which shares genetic and environmental factors. The aim of this project is to analyse simple factors (height, weight, lifestyle changes, etc.) and complex (imbalances of bio hormones and chemicals such as insulin, vitamin D, etc.) factors that contribute to the development of the disease. The data we used for our project was published in Kaggle, written by Prasoon Kottarathil, called Polycystic ovary syndrome (PCOS) in 2020. This database contains records of 543 PCOS patients tested on the basis of 40 parameters. For this, we have used Machine Learning techniques such as Logistic Regression, Decision Trees, SVMs, Random Forests, etc, A detailed analysis of all the items made using graphs and programs and prediction using Machine Learning Models helped us to identify the most important indicators for the same.
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