A Comparative Analysis of SVM and Neural Networks in Medical Diagnosis

Dr. Anupa Sinha

Asst. Professor, Faculty of CS & IT, Kalinga University, Raipur

anupa.sinha@kalingauniversity.ac.in

The use of machine learning algorithms in healthcare has increased dramatically in recent years, transforming the field of medical diagnostics. Two well-liked algorithms that are frequently used in medical decision-making are neural networks (NN) and support vector machines (SVM). Both offer unique benefits and problems, but they have both shown success in various situations. The strengths, drawbacks, and practical applications of SVM and neural networks in medical diagnostics will all be discussed in this article’s comparative comparison.

Understanding SVM and Neural Networks

Support Vector Machines (SVM)  For tasks involving regression and classification, supervised learning techniques called Support Vector Machines (SVM) are employed. The main goal of SVM is to identify the optimal hyperplane for dividing data points into distinct classes. This hyperplane is the ideal border for classification since it optimizes the margin between two classes. Small to medium-sized datasets are ideal for SVM, and it performs best when the data can be linearly separated or converted to linearity using kernel functions.

Neural Networks Neural Networks (NN) represent a class of machine learning models that draw inspiration from the architecture of the human brain. They are made up of layers of networked neurons that analyze information and pick up intricate patterns. Due to their great flexibility, neural networks are capable of capturing non-linear relationships in data.

SVM Application in Medical Diagnosis

Encouragement Since vector machines offer strong categorization skills, they have been used extensively in medical diagnostics. Among their main benefits are:

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  High accuracy with short datasets: Support vector machines (SVMs) are effective in handling high-dimensional data, like gene expression data or radiography pictures, when medical datasets are modest in size.
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  Handling difficult boundaries: SVM may divide classes that are not linearly separable by utilizing kernel functions (such as Polynomial or Radial Basis Function). Since class boundaries are frequently non-linear in medical diagnostics, this property is especially helpful in this area.
  
  • Robustness against overfitting: Support vector machines (SVMs) are ideally suited for diagnostic tasks such as differentiating between cancerous and non-cancerous cells because they are less likely to overfit, particularly in high-dimensional regions.

For instance, the classification of breast cancer
SVM has been effectively used to detect breast cancer by using clinical data or mammography pictures. To determine if a tumor is benign or malignant, the machine learning model is trained using labeled datasets. Due to its capacity to generalize well from tiny, labeled datasets, SVM delivers high accuracy and low false-positive rates in breast cancer detection.

Neural Networks’ Application in Medical Diagnosis

Medical diagnosis has greatly benefited from the revolutionary contributions made by neural networks, especially from Deep Learning models. The following are some of NN’s significant advantages in this field:

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  Great adaptability when working with big datasets: Generous datasets are ideal for neural networks, which can extract complex patterns from massive volumes of data. NN models can process and learn from millions of medical imaging (such as MRI or CT) data points.

Diabetic Retinopathy: Identification using retinal scans, neural networks have proven crucial in the diagnosis of diabetic retinopathy. CNN models have a high degree of accuracy in identifying minute alterations in the retina, including microaneurysms or hemorrhages, thanks to their training on thousands of labeled eye pictures. For people with diabetes, this has improved early detection and treatment outcomes.

Practical Uses

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  SVM in Cancer Detection: Because SVM models can handle smaller datasets with high dimensionality (like gene expression data), they are frequently utilized in the detection of malignancies, including breast and prostate cancer.
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  NN in Radiology: Neural Networks have revolutionized the field of radiology by processing pictures from MRIs, CT scans, and X-rays to identify a variety of illnesses, such as tumors, fractures, and anomalies in the organs.

Conclusion

Neural networks and support vector machines both have strong benefits when it comes to diagnosing medical conditions. When the dataset is smaller and the emphasis is on precise, high-dimensional classification problems, SVM performs exceptionally well. Large datasets and intricate, non-linear diagnostic issues—particularly in picture analysis—are better handled by neural networks, especially deep learning models. In actuality, the selection between these algorithms is frequently influenced by the type of medical data, available computing power, and the particular diagnostic task at hand. Perhaps in the future of healthcare, even more potent diagnostic tools can be unlocked by combining the advantages of both approaches or applying them in hybrid models.