Machine Learning for the Multiple Disease Prediction System
DOI:
https://doi.org/10.32628/CSEIT24103217Keywords:
Symptoms, Data-Driven, K-Nearest Neighbour, Healthcare, Random Forest, Disease Prediction, Support Vector Machine, Machine LearningAbstract
Disease prediction, which aims to identify individuals who are at risk of contracting specific diseases, is a crucial component of healthcare. Recently, machine learning algorithms have shown to be effective tools in the fight against illness prediction due to their superior ability to sort through large datasets in search of complex patterns. The development of Machine Learning (ML) in the contemporary healthcare period has created new opportunities for the diagnosis and treatment of chronic illnesses. In this paper we are proposes a complete Multiple Disease Prediction System that makes accurate predictions of diabetes, cancer, and heart disease using machine learning algorithms. The system's purpose is to analyse intricate medical datasets and find trends and risk factors related to these illnesses. The system uses cardiovascular data analysis and logistic regression to detect heart disease and provide a probabilistic evaluation of heart health. Convolutional Neural Networks, which evaluate medical imaging to find malignancies with high precision, are used to simplify cancer detection. Finally, Support Vector Machines are used to predict diabetes by taking into account a variety of metabolic and genetic indicators to evaluate. Making it simpler for people to detect their own health issues with just their symptoms and exact vital signs is the aim of this project. The proposed approach improves both the predictive power and precision of sickness.
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