Early Prediction of Disease Using Machine Learning: Leveraging Medical Data for Accurate Classification


Authors : Rahul P. Mahajan

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/y3uxnf4n

Scribd : https://tinyurl.com/22bhmaut

DOI : https://doi.org/10.38124/ijisrt/25mar1858

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Abstract : The accurate prediction of diseases at early stages is vital to enhance patient outcomes especially when dealing with fatal conditions such as cancer. The most prevalent cancer that may be lethal if left untreated is lung cancer. Clinical success in diagnosis and treatment depends on discovering health conditions during early stages when treatment remains effective for severe cases. There are a number of methods for predicting cancer severity that make use of deep learning and machine learning. A deep learning approach that utilizes Convolutional Autoencoders (CAEs) performs detection of lung cancer from examinations of histopathology images. The model training for assessing classification performance utilizes LC25000 dataset by adopting advanced preprocessing methods that execute data augmentation along with noise reduction and normalization. The CAE model brings superior performance than standard deep learning techniques CNN, VGG19 and ResNet-50 by attaining accuracy at 99.41% with precision at 98.52%, recall at 98.51% and F1-Score at 98.51%. However, using ROC and Precision-Recall curves, the model shows that it can differentiate between various cancer subtypes. In medical contexts, the study shows that deep learning techniques may accurately identify early lung cancer on a wide scale, leading to better clinical diagnosis.

Keywords : Healthcare, Disease Diagnosis, Clinical Research, Lung Cancer, MRI, X-Ray, CT Scan, Medical Imaging, Machine Learning, LC25000 Data.

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The accurate prediction of diseases at early stages is vital to enhance patient outcomes especially when dealing with fatal conditions such as cancer. The most prevalent cancer that may be lethal if left untreated is lung cancer. Clinical success in diagnosis and treatment depends on discovering health conditions during early stages when treatment remains effective for severe cases. There are a number of methods for predicting cancer severity that make use of deep learning and machine learning. A deep learning approach that utilizes Convolutional Autoencoders (CAEs) performs detection of lung cancer from examinations of histopathology images. The model training for assessing classification performance utilizes LC25000 dataset by adopting advanced preprocessing methods that execute data augmentation along with noise reduction and normalization. The CAE model brings superior performance than standard deep learning techniques CNN, VGG19 and ResNet-50 by attaining accuracy at 99.41% with precision at 98.52%, recall at 98.51% and F1-Score at 98.51%. However, using ROC and Precision-Recall curves, the model shows that it can differentiate between various cancer subtypes. In medical contexts, the study shows that deep learning techniques may accurately identify early lung cancer on a wide scale, leading to better clinical diagnosis.

Keywords : Healthcare, Disease Diagnosis, Clinical Research, Lung Cancer, MRI, X-Ray, CT Scan, Medical Imaging, Machine Learning, LC25000 Data.

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