Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

• Published in: International journal of advanced computer science & applications 2020, Vol.11 (11)
• Main Authors: Khan, Talha Ahmed, A., Kushsairy, Nasim, Shahzad, Alam, Muhammad, Shahid, Zeeshan, Mazliham, M.S
• Format: Article
• Language: English
• Subjects: Algorithms; Blood pressure; Breast cancer; Cardiovascular disease; Cholesterol; Classification; Classifiers; Comparative studies; Deep learning; Discriminant analysis; False alarms; Heart; Heart diseases; Heart rate; Machine learning; Mammography; Medical prognosis; Pain; Paper machines; Regression; Robustness (mathematics); Scientific papers; Support vector machines; Tumors
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2020.0111170

Breast cancer and heart disease can be acknowledged as very dangerous and common disease in many countries including Pakistan. In this paper classifiers comparative study has been performed for the tumor and heart disease classification. Around one lac women are diagnosed annually with this life-threatening disease having no family history of the disease. If it is not treated on time it may grow and spread to the other parts of human body. Mammograms are the X-rays of the breast which can be used for the screening of cancer tumor. Prior identification of breast cancer may increase the chance of survival up to 70 percent. Tumors which causes cancer can be categorized into two types: a) Benign and b) Malignant. Benign tumor can be explained as the tumor which are not attached to neighbor tissues or spread in the other parts of the body. In Malignant tumor, other parts may be affected by it as it can grow and spread in the other parts of the body. To classify the tumor as Malignant or Benign is very complex as the similarities of cancer tumor and tumor caused by the skin inflammation are almost same. The early identification of Malignant is mandatory to protect the patient life. Diversified medical methods based on deep learning and machine learning have been developed to treat the patients as cancer is a very serious and crucial issue in this era. In this research paper machine learning algorithms like logistic regression, K-NN and tree have been applied to the breast cancer data set which has been taken from UCI Machine learning repository. Comparative study of classifiers has been performed to determine the better classifier for the robust prediction of breast tumors. Simulated results proved that using Logistic regression, ninety-one percent accuracy was achieved. The research showed that logistic regression can be applied for the accurate and precise early prediction of breast cancer. Cardiovascular disease is very common throughout the world. It has been noticed that health in cardiac patients that there are so many factors which causes heart disease or heart attack. The factors leading to the heart failure includes varying blood pressure, high sugar, cardiac pain, and heart rate, high cholesterol level (LDL), artery blockage and irregular ECG signals. Many researchers proved that stress in patients can also be the reason for the heart disease. Higher numbers of cardiac surgeries like angioplasty and heart by-pass are performed on annual basis. Actually