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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.