Multiple Disease Prediction System using Machine Learning Algorithm

Machine Learning–based Multi-Disease Prediction

This paper proposes a machine learning–based multi-disease prediction system that integrates disease-specific classifiers within a unified, real-time clinical decision-support platform. The framework employs Support Vector Machine (RBF) for diabetes prediction, Support Vector Machine (linear) for heart disease, Decision Tree for chronic kidney disease (CKD), and Logistic Regression for cancer prediction, with each classifier selected according to the statistical characteristics of its respective dataset. The system is implemented using a Streamlit-based web interface, enabling efficient real-time prediction with interpretable outputs. Experimental evaluation demonstrates strong predictive performance, achieving accuracies ranging from 85.71% to 94.30% and AUC-ROC values between 0.91 and 0.97 across the four disease modules, representing a 7.2 percentage-point improvement over comparable unified prediction systems reported in the literature. The modular architecture provides scalability, low computational complexity, and rapid inference, making it suitable for pre-diagnostic screening in clinical environments. The proposed framework offers an effective and practical solution for early chronic disease detection while supporting future expansion. Planned enhancements include the integration of deep learning models for medical imaging and electronic health records, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME, wearable and IoT-based continuous health monitoring, federated learning for privacy-preserving distributed model training, and prospective clinical validation through hospital information system integration. These developments are expected to improve prediction accuracy, interpretability, scalability, and clinical applicability for next-generation intelligent healthcare systems.

An intelligent disease prediction framework integrating machine learning, optimization, and LLM with RAG

Diabetes Prediction System Using Machine Learning

Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.

A Modified Genetic Algorithm–Based Feature Optimization Framework for Cardiovascular Disease Risk Prediction

The proper diagnosis of cardiovascular disease (CVD) risk rises as a significant issue in clinical practice because the risk is directly associated with patient survival. Thus, correct and timely assessment of the risk is crucial. This work suggests a CVD probability prediction model, which combines ensemble learning and Deep Learning (DL) algorithms with a feature choice approach supported by a Modified Genetic Algorithm (MGA). A balanced clinical dataset consisting of 1,025 patient records and 14 medically relevant attributes was used which was retrieved via a publicly available repository with benchmarks dataset. The comparison of a broad range of predictive models, including classical Machine Learning (ML) algorithms like Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forests, XGBoost, and LightGBM, as well as DL models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multi-layer Perceptrons (MLP), was made. The MGA based feature collection is used to select the most informative clinical attributes, decrease feature redundancy, and increase discriminative ability. The outcomes of experimental findings prove that the models with the assistance of MGA are most effective as they are more predictive and stable across various classifiers. The results demonstrate the imperative need of intelligent feature selection to enhance model generalization and predictability, which reinforces predictive modelling framework with feature optimization against cardiovascular risk prediction. The paper presents the framework that mediates between optimization methods and predictive modeling to provide valuable information on the next generation of data-driven cardiovascular diagnostics.

DeepMed: A Multi-Model Deep Learning Algorithm for Predicting Chronic Diseases

Prognostic modelling is an essential tool in clinical sciences. A machine learning approach can efficiently help with disease prediction and prevention by integrating large amounts of structured patient records. This study develops a deep learning framework to assist in early disease detection and clinical decision-making. A hospital patient dataset was used to develop a predictive model for 13 chronic conditions. The dataset was preprocessed, including the use of a denoising autoencoder neural network for imputations. To improve the predictive ability of the model and reduce overfitting, a novel multi-model approach was used in which ten additional neural networks were trained separately and used to predict the conditions through bootstrapping and bagging techniques. The program was able to predict the 13 conditions with an average accuracy of 96.95% and a micro-averaged F1 score of 0.91 on an independent test set, with the F1 score calculated to account for the presence of class imbalance. This program can potentially aid in enabling early disease prediction, facilitating timely treatment, and improving quality of life while lowering treatment costs and helping hospitals, emergency rooms, caregivers, and individuals with diagnosis predictions, prompt decision-making, and resource prioritization, especially in times of limited healthcare resources.

Transforming Heart Disease Prediction with Advanced Machine Learning Techniques

Heart disease remains the leading cause of mortality globally, necessitating early and accurate detection to improve patient outcomes. This research focuses on the predictive analysis of heart disease using machine learning (ML) techniques, comparing the performance of multiple classifiers to identify the most accurate and least error-prone method. Two datasets from UCI and Kaggle repositories were utilized, each containing 14 attributes related to heart health indicators. Techniques including J48, Naive Bayes, Logistic Regression, Simple Cart, Bagging, Decision Stump, AdaBoost, Artificial Neural Networks, and Support Vector Machine (SVM) were applied. Evaluation metrics such as Mean Absolute Error (MAE), Relative Absolute Error (RAE), accuracy, precision, recall, and F-measure were used for performance comparison. Results revealed that SVM achieved the highest performance on the UCI dataset, while Simple Cart performed best on the Kaggle dataset, offering the highest accuracy and lowest error rates. The research work concludes that ML models, when properly tuned and validated, can significantly assist in the early diagnosis of heart disease, offering critical support for clinical decision-making. Future work may involve hybrid approaches and the use of more recent datasets to further improve prediction accuracy.