ABSTRACT

The diagnosis of diseases such as Non-Communicable Diseases (NCDs) and Cancer is decisive for planning proper treatment and ensuring the well-being of patient. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of Artificial Intelligence (AI) can improve the level of diagnostic accuracy and efficiency in healthcare fields. While the current literature has examined various approaches for diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, the paper portrayed the AI techniques in diagnostics of NCDs and cancer and provide a snapshot to guide future research. This study observed that Deep Learning (DL) approaches have been mostly used for solving issues of NCDs and cancer disease detection in terms of different metrics. However, several issues need to be addressed before the successful application of DL in disease diagnostics can be achieved. Therefore, to end this, an enhanced DL-based system will be proposed in the future study for effective detection of NCDs and Cancer. Keywords: Non-Communicable Diseases (NCDs), heart disease, kidney disease, and diabetes, Lung and Breast cancer, Detection, Machine learning (ML) and Deep Learning (DL)
Publication date: 01/01/2024

https://ijbpas.com/pdf/2024/January/MS_IJBPAS_2024_7673.pdf

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https://doi.org/10.31032/IJBPAS/2024/13.1.7673