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While the classification of medical data poses a formidable challenge, it remains a source of profound fascination within the research community. Its potential to enable precise preventive measures against future ailments is a driving force. Machine learning (ML) algorithms play a pivotal role in this endeavor, and the effectiveness of these algorithms is deeply intertwined with the pre-processing of data. This study seeks to evaluate a range of ML-based models with a specific focus on their utility in predicting Autism Spectrum Disorder in Toddlers (ASD-T) and Chronic Kidney Disease (CKD). The pre-processing techniques encompass a spectrum of procedures such as imputing missing values, feature selection, resampling, and feature scaling. In the pursuit of precision, hyperparameter tuning is employed to fine-tune these models. Subsequently, six classification techniques—Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Extra Tree (ET), and Random Forest (RF)—are executed using a rigorous tenfold cross-validation approach to classify the datasets. The integration of ensemble learning techniques, namely bagging and boosting, enhances performance while safeguarding against bias and overfitting. Although the literature is replete with experimental examples, room for improvement persists. This study distinguishes itself by achieving significantly superior results compared to prior research. Notably, our proposed model, the traditional ET classifier, delivers unparalleled performance, boasting a remarkable 100% accuracy in the ASD-T dataset and an impressive 99.85% accuracy in the CKD dataset.

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From Data to Diagnosis: A Journey with Machine Learning, Hyperparameter Tuning, and Ensemble Learning for Disease Prognostication

  • 会议论文

  • 发表日期：

    2025年

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  • Anwar Hossain Efat,
  • Shahriar Faysal Ferdous,
  • Shajibul Islam Nayem,
  • Azmain Islam Surhyth

摘要

While the classification of medical data poses a formidable challenge, it remains a source of profound fascination within the research community. Its potential to enable precise preventive measures against future ailments is a driving force. Machine learning (ML) algorithms play a pivotal role in this endeavor, and the effectiveness of these algorithms is deeply intertwined with the pre-processing of data. This study seeks to evaluate a range of ML-based models with a specific focus on their utility in predicting Autism Spectrum Disorder in Toddlers (ASD-T) and Chronic Kidney Disease (CKD). The pre-processing techniques encompass a spectrum of procedures such as imputing missing values, feature selection, resampling, and feature scaling. In the pursuit of precision, hyperparameter tuning is employed to fine-tune these models. Subsequently, six classification techniques—Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Extra Tree (ET), and Random Forest (RF)—are executed using a rigorous tenfold cross-validation approach to classify the datasets. The integration of ensemble learning techniques, namely bagging and boosting, enhances performance while safeguarding against bias and overfitting. Although the literature is replete with experimental examples, room for improvement persists. This study distinguishes itself by achieving significantly superior results compared to prior research. Notably, our proposed model, the traditional ET classifier, delivers unparalleled performance, boasting a remarkable 100% accuracy in the ASD-T dataset and an impressive 99.85% accuracy in the CKD dataset.