Development of A Machine Learning Model for Multiple Disease Prediction

Abstract: The growing incident of various diseases globally underscores the urgent need for innovative healthcare solutions. This study focused on developing an improved machine learning-based system for predicting multiple diseases. By evaluating the probability of illnesses using patient data, the primary goal is to aid medical professionals in the early diagnosis and personalized management of conditions such as cancer, diabetes, and cardiovascular diseases. The approach employs supervised learning algorithms to analyze medical datasets and provide accurate disease predictions. Several methods, including decision trees, support vector machines, and neural networks, were explored to identify the optimal model based on accuracy and computational efficiency. The system was trained and validated using diverse medical datasets that were preprocessed to address noise and missing values. The architecture of the system was elaborated, detailing steps such as data preparation, model training, and interpretation of results. The performance of the system was rigorously evaluated using key metrics like accuracy, precision, recall, and F1-score. Findings from this study indicate that this approach can serve as a valuable tool in clinical decision-making, delivering highly accurate predictions for various diseases. This work highlights the potential of machine learning to enhance diagnostic processes, leading to faster and more effective treatments. Future efforts will focus on incorporating real-time data for dynamic updates and extending the system's functionality to predict a broader range of diseases.

Machine learning, multi-disease prediction, supervised learning, healthcare, decision trees, support vector machines, neural networks, medical datasets, diagnostic tool, predictive model

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