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A tremendous amount of research has investigated carcinoma and looked into treatments to lower cancer mortality. Nevertheless, oral cancer is still a considerable health issue worldwide and mortality and incidence rates are rising. Oral cancer is ranked as the sixth most common carcinoma and is a prime health dilemma globally. Thus, it is increasingly important to have tools that allow for discrimination between oral dysplasia and squamous-cell carcinoma. Although conventional methods such as medical imaging can be helpful in prediction and diagnosis of oral cancer, these methods continue to have limitations. More recently, machine learning has been used as a complementary approach in biomedical research and it now plays a leading role in the emerging domains of computational biology and bioinformatics. Specifically, DNA chip gene expression technology is currently helping researchers to distinguish oral dysplasia and squamous-cell carcinoma thru analysis of gene expression in tissue samples. Nonetheless, one of the prime challenges in the computational process of microarray data is the curse of dimensionality due to the crushing number of features. Therefore, we applied a random projection (RP) feature construction technique to tackle the problem of high-dimensional gene expression data and to increase the efficiency of our proposed model. In addition, we combined a RP technique with a support vector machine (SVM) that employs a sequential minimal optimization training algorithm (SMO) in order to efficiently differentiate squamous-cell carcinoma and oral dysplasia. The highest classification accuracy recorded by our proposed model was 95.6332%. We show in this study that using a SMO machine-learning classifier with a RP dimensionality reduction tool can be effective for classifying oral cancer.

Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovative diagnostic noninvasive technologies that may enhance early diagnosis of the disease. One of the most promising techniques for early detection of cancerous cells depends on machine learning based on molecular cancer classification using gene expression profiling data. Current technological breakthroughs in gene expression profiling, specifically with DNA and oligonucleotide microarrays, permit the concomitant analysis of the expression of thousands of genes and also enables surveillance of disease prediction and progression of patient survival at the molecular level. For this reason, we attempted to come up with a machine-learning-based strategy called composite hypercubes on iterated random projections (CHIRP) in order to settle the problem of detection of early NSCLC from DNA biochip gene expression data. Furthermore, we utilized an unsupervised dimensionality reduction approach, named t-distributed stochastic neighbor embedding (T-SNE), to reduce computational complexity and to increase the efficiency of the developed system. The average accuracy obtained by the proposed system in terms of detection and diagnosis of early non-small cell lung cancer was 97.21871%. The empirical results prove that the combination of dimensionality reduction models with machine-learning algorithms can be effectively used for early detection of specific NSCLC tumor type.

Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated domains have made significant strides over recent years. For example, cancer prognosis using machine learning techniques is now a promising area of research. Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. Specifically, DNA chip gene expression technology is a promising tool that can identify cancerous cells in early phases of the disease by examining the gene expression of analyzed instances. Furthermore, microarray technology enables researchers to assay the expression of thousands of genes in parallel. In this paper, we present a Gaussian process regression model in order improve the prediction of survivability of patients with early cervical cancer. Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.

Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Conventional methods of diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms which usually appear in the late stage of cancer. Furthermore, biopsy examinations are highly invasive since tissue samples are required to be extracted from patients. There exist minimally invasive cancer biomarkers in forms of proteins from serum. Nevertheless, existing protein-based diagnosis techniques require labor-intensive analysis compounded by low diagnosis sensitivity. There have indeed been a number of studies to identify novel miRNA-based cancer biomarkers. However, the existing diagnosis techniques using miRNA suffer from low diagnosis accuracy, sensitivity, and specificity. The low diagnosis accuracy and sensitivity of the existing techniques stems from the fact that there is extremely low miRNA count in body fluids. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper proposes to circumvent these problems in data analysis stage with a machine learning technique called Random Forest. The proposed system achieved 93.48 % accuracy for gastric cancer and 100 % accuracy for ovarian cancer. The results are promising and encouraging. Despite much noise contaminated the sample preparation process and low miRNA count in body fluids, the proposed system able to identify miRNA markers responsible for classification of cancer.