Chronic Diseases Prediction Using ML

TL;DR

This paper presents a machine learning approach for early prediction of chronic diseases using diverse datasets, aiming to improve patient outcomes and reduce healthcare costs through early detection and prevention.

Contribution

It introduces a machine learning model trained on multiple datasets with feature engineering, and provides a user-friendly interface for disease prediction and prevention suggestions.

Findings

High accuracy in disease prediction models

Effective feature extraction from diverse datasets

User interface for real-time disease assessment

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.