International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
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EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING MODELS IN DISEASE PREDICTION Punithavathi Arikrishnan1 and Dr. Padmapriya Arumugam2 1 Research Scholar, Department of Computer Science, Alagappa University, Karaikudi,
Sivaganga, Tamil Nadu, 630003, India.
2 Professor & Head, Department of Computer Science, Alagappa University, Karaikudi,
Sivaganga, Tamil Nadu, 630003, India. -------------------------------------------------------------------------***-----------------------------------------------------------------------although developing predictive tools that will be most ABSTRACT- The ability to accurately predict health care
useful for automation of diagnosis procedures. Thereby, the use of machine learning in the prediction of diseases ultimately has the potential to ease the burden on healthcare practitioners through increased accuracy in the diagnoses, reduced costs, and timely interventions Precise prediction of the course of diseases has now become one of the most important tasks. The potential of insights from data within the healthcare sector opens new ways for innovation; hence, this has an increasing interest in utilizing machine learning for various purposes. This work focused on the various machine learning algorithms used for predicting the outcome of four major diseases: Polycystic Ovary Syndrome, breast cancer, heart disease, and lung cancer. The chosen four have been selected because these represent a wide spectrum of disorders that differ in their complexity, prevalence, and importance to public health. For instance, PCOS is one of the most common endocrine disorders among reproductive women, but its sophisticated pathophysiology and numerous symptoms have always made its treatment ignored. Meanwhile, breast cancer has not stopped being a common kind of cancer in all parts of the world, the survival rate being largely affected by the time of its detection. Heart diseases and lung cancers have historically been in the foreground, both being leading causes of morbidity and mortality. Since machine learning algorithms can handle huge and complex data with different data types, such as numerical, categorical, and unstructured data, they are found to be useful in disease prediction. General uses of datasets in healthcare include EHR, clinical measures, imaging data, genetic data, and patient demographics. It will be quite difficult to ensure that the derived patterns in this data are meaningful in order to predict the right state or stage of the disease. Machine learning algorithms generally learn a relation between input features and predefined outcomes by training the models over the labeled data. Data preprocessing and feature selection are the two major process that need to be done before machine learning models applied. One of the significant tasks in machine learning is the method of choosing the features known as Feature Selection, in a dataset that is most relevant and useful for making correct predictions. It is, therefore, very
data outcomes is vital for successful disease management. The purpose of this work is to undertake a complete evaluation of several supervised machine learning algorithms for predicting health outcomes using four distinct datasets: Polycystic Ovary Syndrome (PCOS), Breast Cancer, Heart Disease, and Lung Cancer. The research includes preprocessing the data, collecting the most significant features, and training seven prominent machine learning models: Random Forest, K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Logistic Regression, CatBoost, XGBoost, and LightGBM. The models were tested using important performance metrics such as accuracy, precision, recall, and F1score. Additionally, the training and prediction times of the models were also considered to assess their practical applicability. The results showed that among all the models, Logistic Regression consistently outperformed the other models across the datasets, achieving the highest average accuracy, precision, recall, and F1score. The model also exhibited relatively low training and prediction times, making it a promising choice for real-world healthcare applications. Other top performing models included Random Forest and XGBoost, which also exhibited strong predictive capabilities. The findings of this study provide valuable insights into the comparative strengths and weaknesses of various ML algorithms for predicting health outcomes. The results can inform the selection of appropriate models for specific healthcare tasks, ultimately contributing to the development of more accurate and efficient decision support systems in the healthcare area.
Keywords: Machine Learning, PCOS, Heart disease, Breast cancer, Lung cancer, Logistic Regression
1. INTRODUCTION Machine learning, which is a branch of artificial intelligence, is developed to train algorithms on patterns in previous data so that it can predict the future and make decisions on its own without being explicitly programmed. Its application in healthcare has completely revolutionized various approaches toward the prediction of diseases by different researchers and practitioners,
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