Chronic Diseases Prediction Using ML
Sri Varsha Mulakala, G. Neeharika, P. Vinay Kumar, A. Bhargava Kiran
TL;DR
This study addresses multi-disease prediction by integrating machine learning and deep learning into a single framework with a Streamlit-based UI. It combines traditional ML (notably Random Forest) and CNN-based approaches to predict diabetes, heart disease, lung cancer, and brain tumors using disease-specific datasets (Pima, Kaggle health data, histopathology images, and Br35H MRI). The results show diabetes and heart disease achieving high accuracy with RF, while lung cancer and brain tumor classifications achieve strong deep learning performance (CNN and VGG-16), underscoring the value of a hybrid, multi-modal pipeline for early diagnosis and informed prevention. The work highlights practical deployment potential and sets a foundation for expanding to additional diseases and datasets in clinical contexts.
Abstract
The recent increase in morbidity is primarily due to chronic diseases including Diabetes, Heart disease, Lung cancer, and brain tumours. The results for patients can be improved, and the financial burden on the healthcare system can be lessened, through the early detection and prevention of certain disorders. In this study, we built a machine-learning model for predicting the existence of numerous diseases utilising datasets from various sources, including Kaggle, Dataworld, and the UCI repository, that are relevant to each of the diseases we intended to predict. Following the acquisition of the datasets, we used feature engineering to extract pertinent features from the information, after which the model was trained on a training set and improved using a validation set. A test set was then used to assess the correctness of the final model. We provide an easy-to-use interface where users may enter the parameters for the selected ailment. Once the right model has been run, it will indicate whether the user has a certain ailment and offer suggestions for how to treat or prevent it.