Asim Ali Khan

This research study addresses the critical global health issue of heart disease (HD), emphasizing the importance of early detection for improving recovery outcomes. The authors have applied various machine learning (ML) algorithms, including logistic regression (LR), linear discriminant analysis (LDA), Gaussian naive bayes (GNB), support vector machine (SVM), and XGBoost (XGB) to classify the Statlog and Cleveland HD datasets. Performance metrics such as accuracy, precision, recall, F1-score, specificity, and Cohen’s kappa have been evaluated across these HD records. This study conducted two experiments: one using default ML classifiers and another with a hybrid genetic algorithm ML (GA-ML) model. The GA has been employed as a feature selector (FS), significantly enhancing the performance of each default classifier by selecting 9 out of 13 features. Notably, the GA-XGB model achieved the highest performance with an accuracy of 94.83%, precision of 93.33%, sensitivity of 96.55%, F1-score of 94.52%, specificity of 93.10%, Cohen’s kappa of 0.90, a positive likelihood ratio (LR[ ]) of 14, a negative likelihood ratio (LR-) of 0.037, and a diagnostic odds ratio (DOR) of 378 on the combined HD dataset. These results have been validated using a 10-fold cross-validation technique. A comparative analysis has been conducted with default ML classifiers, hybrid GA-ML classifiers, and state-of-the-art methods. The results of the GA-ML models confirm the superiority of the proposed method, offering valuable insights into advancing early detection strategies and improving heart health care outcomes.

Breast cancer has become a menacing form of cancer among women accounting for 11.6% of total deaths of 9.6 million due to all types of cancer every year all over the world. Early detection increases chances of survival and reduces the cost of treatment as well. Screening modalities such as mammography or thermography are used to detect cancer early; thus, several lives can be saved with timely treatment. But, there are interpretational failures on the part of the radiologists to read the mammograms or thermograms and also there are interobservational and intraobservational differences between them. So, the degree of variations among the different radiologists in the interpretation of results is very high resulting in false positives and false negatives. The double reading can reduce the human errors involved in the interpretation of mammograms. But, the limited number of medical professionals in developing or underdeveloped countries puts a limitation on this remedial way. So, a computer-aided system (CAD) is proposed to detect the benign cases from the abnormal cases that can result in automatic detection of breast cancer or can provide a double reading in the case of nonavailability of the trained medical professionals in developing economies. The generally accepted screening modality is mammography for the early detection of cancer. But thermography has been tried for early detection of breast cancer in recent times. The high metabolic activity of the cancer cells results in an early change in the temperature profile of the region. This shows asymmetry between normal and cancerous breast which can be detected using different techniques. Thus, this work is focussed on the use of thermography in the early detection of breast cancer. An experimental study is conducted to find the results of classification accuracy to compare the efficacy of thermography and mammography in classifying the normal from abnormal ones and further abnormal ones into benign and malignant cases. Thermography is found to have classification accuracy almost at par with mammography for classifying the cancerous breasts from healthy ones with classification accuracies of thermography and mammography being 96.57% and 98.11%, respectively. Thermography is found to have much better accuracy in identifying benign cases from the malignant ones with the classification accuracy of 92.70% as compared to 82.05% with mammography. This will result in the early detection of cancer. The advantage of being portable and inexpensive makes thermography an attractive modality to be used in economically backward rural areas where mammography is not practically possible.