Yash Karnani
Jr. Research Engineer
Yash Karnani is currently working as a Junior Research Intern, where he is exploring the clinical applications of wearable devices and developing methods to extract information from medicine strips for potential use in health monitoring and medical diagnostics.
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He is pursuing a B.Tech. in Computer Science and Internet of Things (IoT) at GITAM University. His academic background encompasses Artificial Intelligence, IoT, and Data Science, and he has worked on various projects involving computer vision and web development. Yash has also completed an internship as a Java Developer at Woodpecker Analytics, where he gained hands-on experience in software development.
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His current role at Rhenix has further enhanced his skills in AI and data analytics, allowing him to contribute to innovative solutions in the healthcare domain. Yash is passionate about leveraging technology to drive meaningful advancements in data-driven decision-making and healthcare innovation.
Stories
Summary "Machine learning approaches to identify Parkinson's disease using voice signal features"
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including subtle changes in speech. Traditionally, PD diagnosis relies on motor symptom observation ...
Summary "Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology"
This study developed machine learning-based models to predict postoperative mortality in patients with gastric cancer, utilizing clinical and inflammatory markers. Six algorithms were tested, with Random Forest and Logistic Regression ...
Summary "Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models"
This study applied machine learning to assess lifestyle and occupational risk factors for bladder cancer. A balanced case-control study of 692 bladder cancer patients and 692 healthy controls was conducted using ...
Videos
Machine learning approaches to identify Parkinson's disease using voice signal features | Cell talk
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including subtle changes in speech. Traditionally, PD diagnosis relies on motor symptom observation...
Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology | Cell talk
This study developed machine learning-based models to predict postoperative mortality in patients with gastric cancer, utilizing clinical and inflammatory markers. Six algorithms were tested, with Random Forest and Logistic Regression ...
Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models | Cell talk
This study applied machine learning to assess lifestyle and occupational risk factors for bladder cancer. A balanced case-control study of 692 bladder cancer patients and 692 healthy controls was conducted using ...
Deep learning integrates histopathology and proteogenomics at a pan-cancer level | Cell talk
Machine learning and artificial intelligence are transforming cancer research and precision medicine. Computational pathology now uses deep learning models like convolutional neural networks ...
