Abstract
According to WHO, the incidence of life-threatening diseases like cancer, diabetes, and Alzheimer’s disease is escalating globally. In the past few decades, traditional methods have been used to diagnose such diseases. These traditional methods often have limitations such as lack of accuracy, expense, and time-consuming procedures. Computer-aided diagnosis (CAD) aims to overcome these limitations by personalizing healthcare issues. Machine learning is a promising CAD method, offering effective solutions for these diseases. It is being used for early detection of cancer, diabetic retinopathy, as well as Alzheimer’s disease, and also to identify diseases in plants. Machine learning can increase efficiency, making the process more cost effective, with quicker delivery of results. There are several CAD algorithms (ANN, SVM, etc.) that can be used to train the disease dataset, and eventually make significant predictions. It has also been proven that CAD algorithms have potential to diagnose and early detection of life-threatening diseases.
Authors Pramod Kumar and Sameer Ambekar have been equally contributed to this chapter.
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Kumar, P., Ambekar, S., Roy, S., Kunchur, P. (2019). Computer-Aided Diagnosis of Life-Threatening Diseases. In: Paul, S. (eds) Application of Biomedical Engineering in Neuroscience. Springer, Singapore. https://doi.org/10.1007/978-981-13-7142-4_14
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DOI: https://doi.org/10.1007/978-981-13-7142-4_14
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