A Systematic Literature Survey on A Smart Multidisease Prediction and Doctor Recommendations using ML

A Systematic Literature Survey on A Smart Multidisease Prediction and Doctor Recommendations using ML

Vivek Kumkar, Rutik Pawar,  Aditya Kale

Prof. Ashwini Gavade,

Department of Information Technology Sinhgad Institute of Technology and Science, Narhe

Pune, India

Abstract-Smart multidisease prediction and doc- tor recommendations using machine learning is a growing field of research. The proposed methodol- ogy seeks to deliver precise illness prediction for several diseases, which may have important reper- cussions for enhancing the precision and timeli- ness of medical diagnostics and improving patient outcomes. Machine learning algorithms like Naive Bayesian networks,CNN, KNN are used to properly predict and classify diseases based on patient symp- toms and medical data analysis. Several studies have proposed models that can predict multiple diseases such as diabetes, heart disease, chronic kidney dis- ease, and cancer. The ultimate goal is to provide healthcare practitioners with the tools to make ed- ucated decisions and deliver improved patient care. This paper presents a survey of research papers and articles on the topic of smart multidisease prediction and doctor recommendations using machine learn- ing.
Keywords-Machine   Learning,   CNN,   Python-

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