An optimized efficient combinatorial learning using deep neural network and statistical techniques

Article type: Research Article

Authors: V K, Jyothi^* | Sarma, Guda Ramachandra Kaladhara

Affiliations: HCL Technologies Bangalore, India

Correspondence: [*] Corresponding author: Jyothi V K, HCL Technologies Bangalore, India. E-mail: dr.jyothivk.ai@gmail.com.

Abstract: Research work is to discover the rapid requirement of Artificial Intelligence and Statistics in medical research. Objective is to design a diagnostic prediction system that can detect and predict diseases at an early stage from clinical data sets. Some of major diseases leading reasons of death globally are heart disease and cancer. There are different kinds of cancer, in this study we focused on breast cancer and heart disease. Prediction of these diseases at a very early stage is curable and preventive diagnosis can control death rate. Designed two Artificial Intelligence systems for prediction of above-mentioned diseases using statistics and Deep neural networks (i) Combinatorial Learning (CLSDnn) and (ii) an optimized efficient Combinatorial Learning (eCLSDnn). To evaluate the performance of the proposed system conducted experiments on three different data sets, in which two data sets are of breast cancer namely, Wisconsin-data set of UCI Machine Learning repository and AI for Social Good: Women Coders’ Bootcamp data set and Cleveland heart disease data set of UCI Machine Learning repository. The proposed architectures of binary classification are validated for 70%–30% data splitting and on K-fold cross validation. Recognition of Malignant cancerous tumors CLSDnn model achieved maximum accuracy of 98.53% for Wisconsin data set, 95.32% for AI for Social Good: Women Coders’ data set and 96.72% for Cleveland data set. Recognition of Malignant cancerous tumors eCLSDnn model achieved 99.36% for Wisconsin data set, 97.12% for AI for Social Good: Women Coders’ data set and 99.56% for the Cleveland heart disease data set.

Keywords: Heart disease prediction, breast cancer prediction, data mining, deep learning, machine learning

DOI: 10.3233/HIS-230007

Journal: International Journal of Hybrid Intelligent Systems, vol. 19, no. 3,4, pp. 111-128, 2023