Application of SVM in Breast Cancer Detection

Published by Analyttica

Compared to the 1900s, it is safe to assume that, at present, the world healthcare system is at its peak. Modern medicine is making ground-breaking discoveries and tackling deadly diseases every step of the way. But no matter what, the battle against breast cancer keeps on raging against women globally.

What does breast cancer entail?

This common form of malignancy denotes the alteration in the breast tissues caused by a clump of maladies, resulting in their uncontrollable growth. The mutation of the DNA and RNA tends to be the leading cause of these alterations. As for their point of origin, the inner lining of the lobules, commonly referred to as the milk ducts, is usually the primary target. In addition, two major strains of breast cancer haunt women globally:

  • Non-invasive breast cancer – In this particular strain, the cancer cell stays confined to the lobules and does not infiltrate the surrounding fatty and connective breast tissues. These are commonly associated with benign tumors.
  • Invasive breast cancer – In contrast, the invasive strain drives the cancer cells to break through and penetrate the surrounding fatty and breast tissues. These are essentially found in malignant tumors.

Probing deeper into the signs, symptoms, types, and causes of breast cancer is crucial. However, there is another segment that is swiftly becoming the talk of the medical community – the improved detection and diagnosis of breast cancer using Machine Learning techniques.

How Machine Learning is making a difference

In the fight to preserve humankind, Machine Learning is swiftly becoming an ally. It is revolutionizing everything from detecting and diagnosing life-threatening diseases to optimizing surgeries and assisting in patient recovery processes. Its ability to curb healthcare expenses and bring quality medical amenities to all is no less than a miracle.

In recent years, ML as a modeling approach has undoubtedly assisted in extracting knowledge from datasets and identifying hidden patterns within. This, in turn, can help predict different diseases by leveraging certain target variables.

Nevertheless, coming up with a predictive model that can address all known risk factors was posing to be a major challenge. The factors include a lack of physical fitness, alcohol use, hormone replacement therapy after reaching menopause, early menstrual period, ionizing radiation, late pregnancy, and advanced age. And this is where Support Vector Machine (SVM) comes in.

The backing of SVM

SVM marks one of the leading supervised learning algorithms which can be adopted for classification and regression problems. In the most generic sense, SVM denotes a learning machine that can be used for recognizing patterns via data classification and function approximation owing to its generalization abilities.

Be that as it may, SVM can seem like a challenge. Leveraging its predictive accuracy to assess life-threatening conditions such as breast cancer would require working knowledge of the algorithm. LEAPS by Analyttica has dedicated a sample project with the analytical purpose of identifying malignant and benign tumor patterns using hospital cancer data. It would allow data practitioners from the healthcare department to develop as well as work on a predictive model based on a variety of tumor characteristics. Feel free to browse LEAPS to know more.