Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3681 2018 2018 Computer Science and System Engineering, AU-TDR-HUB, Andhra University, Visakhapatnam, India; Department of Information Technology, ANITS (A), Visakhapatnam, India K. K. Scientist-E, NSTL(DRDO), Visakhapatnam, India Suribabu Korada In medical diagnosis and prognosis, symptoms provided by patients play a critical role in identifying diseases. Machine learning offers a powerful approach to analyzing and predicting illnesses based on these symptoms. In particular, classification algorithms are widely used to analyze input data and predict disease outcomes. A key factor in effective classification is the selection of relevant attributes, which directly affects the accuracy of the prediction. This research emphasizes the importance of proper feature extraction techniques in the context of disease prediction using biomedical signal analysis. Effective analysis requires both the extraction of critical features and the elimination of irrelevant data. The aim of this study is to explore existing approaches to disease prediction based on biomedical signal analysis. We focus on feature extraction from pre-processed data, which aids in distinguishing between different biomedical signals recorded by medical devices. Our objective is to identify biomedical cues that differentiate various health conditions. Examples of such signals include electroencephalogram (EEG), electrocardiogram (ECG), and electrogastrogram (EGG). Understanding how these signals differ between healthy and diseased states is crucial for accurate disease prediction. This research investigates diseases such as heart disease, kidney failure, and lung infections, considering how variations in biomedical signals can be used to predict the likelihood of severe illness. We continue to seek advancements in predicting and mitigating future health risks 2025 2025 315 327 10.54216/FPA.190223 https://www.americaspg.com/articleinfo/3/show/3681