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Articles: 0
Papers: 2

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Researchcancer

Enhancing decision-making for breast cancer through advanced machine learning and data analytics

Sahil Aggarwal, Priyanshu Aggarwal, Satyam Chauhan et al. · 2024

Advancements in machine learning and data analytics have transformed the landscape of biomedical decision-making, offering innovative solutions to address the complexities of diagnosing, prognosing, and planning treatments for breast cancer. Breast cancer is a significant health concern, necessitates precise and timely decision-making in diagnosis, prognosis, and treatment. This research endeavours to advance decision-making in breast cancer care through the application of cutting-edge machine learning and data analytics techniques. The context of this research is the urgent need for improved breast cancer management. Current approaches, while invaluable, face inherent complexities in handling diverse data sources and tailoring treatments to individual patients. Advanced machine learning and data analytics offer the potential to mitigate these challenges. This paper provides a comprehensive examination of the application of machine learning and data analytics in the realm of breast cancer. We begin by delving into the sources of biomedical data and their pre-processing, subsequently exploring a range of machine learning algorithms and feature engineering methods. Our primary objectives are to develop highly accurate diagnostic models for breast cancer, to predict disease progression through advanced prognosis models. This research also underscores the importance of model interpretability and ethical considerations, promoting transparency and equity in the application of artificial intelligence in clinical practice. Our findings reveal the potential for advanced machine learning and data analytics to significantly enhance decision-making in breast cancer care.

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ResearchGangrene

CNN sight: Precision detection in gangrene diagnostics

Priyanshu Aggarwal, Sahil Aggarwal, Harshita et al. · ResearchGate · 2024

Gangrene, a serious medical disorder in which bodily tissue dies, provides serious health concerns and need an early diagnosis for successful treatment. In this paper, a novel method for gangrene detection that makes use of Convolutional Neural Networks (CNNs) is proposed. The outstanding performance that CNNs have shown in image identification tasks makes them suitable for use in medical imaging applications.A complete and all-encompassing summary of gangrene is given in the first section of this paper. It distinguishes between the nu- merous gangrene kinds, such as dry, wet, and gas gangrene, each of which presents different obstacles in diagnosis and treatment. We investigate the many reasons, highlighting the mul- tifaceted character of this syndrome, including diabetes, trauma, infections, and peripheral artery disease (PAD). In order to emphasise how complicated the causes of gangrene is, addi- tional risk factors including weakened immune systems and smoking are also mentioned.The vital value of early identification in properly controlling gangrene is emphasised. The necessity of detecting gangrene in its early stages is highlighted by the development of tissue destruction and possible consequences, including systemic infections and the requirement for amputations. A prompt intervention not only enhances patient outcomes but also lessens the total load of ad- vanced cases on the healthcare system.

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