Hemoglobinopathies are a group of disorders in which the hemoglobin molecule has abnormal production or structure. The hemoglobin molecules in red blood cells (RBC) are impacted by the blood disease known as sickle cell disease (SCD), and Thalassemia is one of the major monogenic disorders that reduces hemoglobin production( Kohne et al ., 2011). This disorder results in a large number of red blood cells being destroyed, leading to anemia. India bears a huge burden of hemoglobinopathies; thalassemia is the most prevalent (Mondal SK et al., 2016). A key component of thalassemia prevention is a successful screening procedure to identify Thalassemia carriers. Effective screening programs have numerous obstacles, especially in environments with limited resources. Machine learning (ML) has been used to solve technical and domain-specific problems in a variety of prognostic and diagnostic medical jobs. In this work, we aimed to identify and analyze the most common mutation of β--thalassemia and sickle cell disease from the north Indian population by employing Machine learning-based algorithms. To accurately predict the carrier state from a simple blood test, and to predict pathogenic hemoglobin variants in a group of individuals, these results demonstrate the application of integrated bioinformatics and machine learning approaches. This study contributes to the validation of the models based on data from several individuals and hemoglobinopathies.

Delayed cancer detection is one of the common causes of poor prognosis in case of many cancers including the cancers of the oral cavity. Despite improvement and development of new and efficient gene therapy treatments, very little has been done to algorithmically assess the impedance of these carcinomas. In this work, we attempt to annotate viable attributes in oral cancer gene datasets for identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated to other forms of oral cancer detection.